Adjustment of nursing home quality indicators
2010

Richard N Jones
John P Hirdes
Jeffrey W Poss
Maureen Kelly
Katharine Berg
Brant E Fries
John N Morris
Adjustment of nursing home quality indicators

BMC Health Services Research
10962010RESEARCH ARTICLE BioMed Central Research article
Background: This manuscript describes a method for adjustment of nursing home quality indicators (QIs) defined using the Center for Medicaid & Medicare Services (CMS) nursing home resident assessment system, the Minimum Data Set (MDS). QIs are intended to characterize quality of care delivered in a facility. Threats to the validity of the measurement of presumed quality of care include baseline resident health and functional status, pattern of comorbidities, and facility case mix. The goal of obtaining a valid facility-level estimate of true quality of care should include adjustment for resident-and facility-level sources of variability.Methods:We present a practical and efficient method to achieve risk adjustment using restriction and indirect and direct standardization. We present information on validity by comparing QIs estimated with the new algorithm to one currently used by CMS.Results: More than half of the new QIs achieved a "Moderate" validation level.Conclusions: Given the comprehensive approach and the positive findings to date, research using the new quality indicators is warranted to provide further evidence of their validity and utility and to encourage their use in quality improvement activities.

Background

In 1986, the Institute of Medicine Committee on Nursing Home Regulation made recommendations to Congress [1] to improve quality of care in nursing homes (NH). One was the systematic collection of standardized data on all NH residents: a minimum data set. Under this mandate, the U.S. Center for Medicare and Medicaid Services (CMS) implemented the Resident Assessment Instrument -Minimum Data Set (MDS). All U.S. longterm care (LTC) facilities must complete standardized MDS assessments of each resident to participate.

The MDS includes a clinical assessment of over 400 items covering demographics, medical condition, cognitive, physical, emotional and social functioning, medical diagnoses, therapies, treatments and medication use. The aggregation of individual MDS assessments into archives can be used to generate representative data sets used for prospective payment systems [2], monitoring [3,4] and improving [5][6][7] quality of care. Inspections were targeted based upon continuous collection of resident characteristics. Quality indicators (QIs), computed from resident-level clinical data, are aggregated to facility level and used for targeting facilities for review [8,9]. Following its implementation in the United States, the MDS has been adopted in a number of other countries. The Canadian Institute for Health Information's (CIHI) Continuing Care Reporting System is the data warehouse for MDS data from eight Canadian provinces/territories. QIs, in raw form, are fractions derived from a numerator (number of residents with a particular outcome) and a denominator (number of residents at risk for the outcome and not otherwise excluded from the QI). QIs may include risk adjustment procedures, including restrictions or exclusions and covariate adjustment. QIs can be used by the facility to target care problems for continuous quality improvement efforts [10].

There are challenges in deriving QIs from resident assessment data. First, differences in types of residents living in facilities make direct comparisons difficult. Second, specification of the care recipient population whose outcome data reflect quality of care must be carefully considered. For example, some residents may be admitted for relatively short term rehabilitative care, short term respite care, or long term custodial care. The clinical trajectories of such different patient populations and differ-ences in their mix across facilities lead to different expectations regarding facility QI performance. Third, many relevant NH outcome measures are rare, resulting in imprecise estimates. Finally, although the MDS is a standardized data collection instrument, facilities may measure outcomes differently or with varying sensitivity [11].

Risk Adjustment

Adjustment for characteristics of residents permits fairer comparisons and improves identification of facilities with quality problems. Some resident characteristics increase risk of adverse outcomes independent of quality of care.

Risk Adjustment Models

There are two main approaches to risk adjustment of nursing home QIs: stratification and indirect standardization. A third, multilevel modelling, has not yet migrated from academic to applied settings [12]. Stratification involves identification of discrete risk groups and computing QIs separately within each group (strata). Strengths of this approach include transparency and computational simplicity. Disadvantages include coarseness of adjustment. Coarse adjustment may result in residual confounding, the problem that differences across facilities may exist within broadly defined strata. Stratification also leads to QIs with small denominators, exacerbating issues of measurement precision and stability when event rates are low. Stratification was advocated by one of the first major operational systems of nursing home QIs [3,13]. Throughout this manuscript we refer to this approach as the first generation approach to QI scoring.

Indirect standardization develops risk adjustment using a multivariable regression approach. It compares observed to expected QI event rates across facilities. Expected rates are based on computations using the results of logistic regression models in a standard (typically the complete) sample of nursing homes. Covariates included in such a model are limited by clinical relevance, appropriateness, and general confounder selection issues [14]. Early approaches to comparing observed and expected QI event rates involved computing ratios of observed to expected proportions [15] although more recent algorithms use differences in proportions in the log odds scale [4,16,17]. Throughout this manuscript we refer to this approach as second generation adjusted QIs.

A New Approach to Risk Adjustment

The overall goal of this manuscript is to presents a new method for risk adjustment (a third generation algorithm). Our method includes the desirable properties of existing methods. The method includes: restriction (excluding residents that are not reflective of the quality of care delivered by the nursing home, e.g., new admis-sions), indirect standardization (multivariable adjustment for carefully selected individual resident characteristics and exclusive of measures of process or structure) and stratification with direct standardization (i.e., it goes beyond reporting strata-specific QI scores by aggregating strata-specific scores into a single composite). Our research questions involves comparing the reliability and validity of QIs scored using the third generation method versus the second generation method.

Our original motivation for moving beyond indirect standardization was driven by two main factors. The first was the development of QIs that could be considered double-barrelled (e.g., measuring both decline and failure to improve). Our second motivation was driven by the distributional properties of many QIs. Since many outcomes are discontinuous and have a truncated distribution (e.g., level of depressive symptoms), facilities with different means at baseline will face differing rates of decline on the basis of chance. Therefore, stratification becomes an important aspect of QI development for fair comparison of facilities.

Although not a main goal of this manuscript, we also provide definitions for and evaluation of a much expanded array of nursing home QIs. The need for an expanded array of QIs is that nursing home residents represent a diverse population with heterogeneous clinical profiles. As such, the broader the conceptualisation of domains of quality, the more inclusive the operationalization of quality will be of individual patients. Moreover, fundamental validity questions for individual QIs for different purposes remain unanswered, and it is probable that different implementations of QIs within clinical domains will prove to vary in their suitability for policy and practice.

Methods

Design, Setting and Residents

Several data sets from the United States and Canada are used in this paper. The data source used initially to derive the 79 nursing home chronic care QIs was a 209 nursing home sample from six states (California, Illinois, Missouri, Ohio, Pennsylvania and Tennessee) covering the third quarter of 2001 and first and second quarters of 2002. This sample was created within the CMS Mega QI study [4]. The 79 QIs were based on all MDS assessments at these facilities during this period. Program process data were collected as part of the Mega QI study, details of which are reported elsewhere [4,18,19]. Participating facilities allowed trained research nurses to interview management staff, observe interactions and abstract a sample of up to 30 records. States were selected for regional representation and numbers of facilities. Facility selection was stratified based upon volume of post-hospital discharge sub-acute care provided as indicated by whether the facility was hospital based. A total of 338 free-standing facilities were approached about participating in the study and accrual was terminated after 209 agreed to participate. Data collection averaged between two and three days per facility and resulted in a validation sample size of 5,738 residents.

It is important to note that although we use data collected under previous contract work with CMS to develop our new quality indicators, the results and methods we describe here are completely original. The regression coefficients and stratification weights used in our adjustment process are not part of the previous work. Interested readers may consult the technical reports from the Mega-QI study for additional details on the sample recruitment and characteristics, and the rationale and development procedure for second-generation quality indicators. Technical details on our adjustment procedure, including computer syntax with regression and stratification weights, are available upon request.

The data source used to create the adjustment models and report on cross jurisdiction distributions of measures consists of facilities in four U.S. states and two Canadian Provinces. The work in generating these data sets and specifying this analysis framework was supported by grants and contracts from the U.S. government [20], several U.S. States [21], and the Canadian Institute for Health Information (CIHI). In each of these data sets, a full panel of MDS data were available, representing up to 3,294 U.S. facilities and 92 Canadian facilities.

Construction of Third-Generation Quality Indicators

Our method for computing QIs extends methods derived from and currently used by CMS, that is documented in a technical report prepared by Abt Associates, Inc. [16]. It compares the proportion of observed and expected residents within a nursing home triggering the QI as differences in a log odds scale. Coefficients used in computing estimated QI scores are fixed, having been estimated from a standard data set (e.g., the full US nursing home population for a fixed period). Each QI has a unique set of covariates and restrictions. Detailed information on each of the third generation QIs is provided in Additional file 1. Computer code is available upon request.

The new third generation QI adjustments refine the selection of covariates and extend the stratification. First, covariates are improved through using a refined set of measures in each model relating outcomes with baseline characteristics, and introducing more powerful summary scales whenever possible (e.g., a summary measure of ADL status in place of individual ADL items). Second, we added a composite stratification variable to each QI model. Numerous candidate measures were reviewed, including summary measures of ADLs, cognition, and the Resource Utilization Groups (RUG-III) case mix algo-rithm. Residents are sorted into low, middle and high risk groups based on thresholds set at the 20 th and 80 th percentile of key stratification variable distribution determined from analysis of a cross national standardization sample. Details on the threshold values can be obtained by request as part of the technical specification and computer code for the QIs upon request. Indirect standardization is performed within strata. Regression coefficients for each stratum are then used to compute expected numbers of residents triggering the QI in a given facility. Finally, strata-specific expected scores are combined using weights from the standard population (see example in Additional file 2).

As with second generation QIs, inference on relative quality of care reflected by third generation adjusted QI is not placed on the absolute value of the adjusted QI, or even the value of the adjusted QI relative to the raw QI. Inference of quality is based on the QI score relative to the overall sample mean used in the standardization procedure.

For our analyses, the standard population used was represented by the large multi-state and a two-province database of resident data from nursing homes (from the U.S.) and complex continuing care hospitals/units in Canada (N~170,000). The standard population affects all facilities equally. Ideally the standard population will be broadly representative of each target facility that is the focus of quality monitoring or research question. This database was used for construction of reference population means, regression coefficients, and weights used in the adjustment procedure. Complete specification of our QIs are available in Additional file 1, computer code is available upon request.

Assessment of Reliability

The new QIs are tested for reliability and validity. When reporting QI scores for use in surveys or internal activities, the assumption is that rates derived from assessment in the prior period reflect current status. Thus, it is important to evaluate the reliability or stability of QIs over time. Reliability was assessed with quarter-to-quarter autocorrelation coefficients.

Assessment of Validity Field Data Collection

As part of the Mega QI study, data collection in each facility included independent data describing the process of care at facility and individual resident level. Details on the validation field study are presented elsewhere [4]. We summarize aspects of the field study here. Data collection (2001)(2002). Teams of trained nurse researchers visited sampled facilities, and completed Medical Record Review of the charts of residents representing the 30 most recently completed MDS assessments. Nurse researchers also completed a partial MDS 2.0 assessment on each sampled resident, and Environmental Walk Through and Resident Observation survey. In addition, an Administrative Questionnaire was delivered to administrators and/ or directors of nursing. The purpose of the Medical Record Review (MRR) was to obtain information regarding the care processes and types of patient/resident assessments performed by sampled facilities on select areas. Twenty-one care areas (or quality dimensions) were reviewed during the MRR (cognitive impairment, communication, delirium, depression/mood, behavior problems, ADL improvement, ADL decline, mobility/ walking, falls, anti-psychotic drugs, pain, physical restraints, feeding tubes, undernutrition/low BMI/weight loss, indwelling urinary catheter, bladder incontinence, bowel incontinence, infections, pressure sores/potential for skin breakdown, burns, abrasions, skin tears, and little or no involvement in activities). For each of these domains, nurse assessors reviewed the medical record (including nursing progress notes, physician orders and progress notes, care plans, therapy consults and notes, medication administration records, flow sheets and other interdisciplinary notes and consults) for resident care and status documentation. Assessors looked for documentation on comprehensive assessments, problems/issues, change in status (within certain time frames), referrals, treatments and nursing care plans. A MDS Supplement was used to conduct assessments on all patients in the sample, including assessment areas from the MDS in selected areas (cognitive patterns, communication/hearing patterns, mood and behavior patterns, physical functioning and structural problems, continence, disease diagnoses, health conditions, oral/nutritional status, skin conditions, activity pursuit patterns, medications, special treatment procedures, and discharge potential and overall status). The Administrative Questionnaire included questions regarding staff responsibilities, staff/resident/ family involvement in care, resident status, access to specialists/consultants, clinical communication channels, staff turnover, staffing ratios, planning processes, information on the organization; and training and orientation of staff. The Environmental Walk Through/Resident Observation was used to gain an overall understanding regarding whether the facility is "resident-centered", what the "feel" of the facility, and what the nature of staff-resident interactions. A series of general environmental measures were employed to describe the responsiveness of the milieu to resident strengths, needs, and problems that include general care environment measures (e.g., nature of physical environment, communication strategies, environmental manipulation and resident interactions with staff ). These measures were collected through assessment, surveillance, and observation of staff technique.

The nurse researchers recorded observations three times per day.

Nurse Researcher Qualifications, Training and Reliability

Peer Review Organizations (PROs) in participating states were contracted to hire field data collectors, with priority for registered nurses (ultimately only 1 was not an RN) with chart review experience and experience in a longterm care setting and/or in completing the MDS Version 2.0. Nurse researchers attended a five-day training and certification program led by the Mega-QI CMS Project Officer, Steering Committee members (including two RNs), five experienced RN researchers with experience in similar data collection activities. A training manual was developed and each assessor was provided a copy. Half of the training program was devoted to training in how to conduct resident assessments using a subset of items from MDS Version 2.0. To certify competency, each trainee completed a case and met individually with the lead trainer for review. To enhance and maintain consistency in coding, project staff held weekly one-hour conference calls with the assessors during the course of data collection. Minutes of the calls. Reliability among nurse researchers was assessed by having nurses complete two paired assessments and medical reviews with their partner per facility. Agreement statistics for the MDS interrater reliability of nurse researchers were very good (average kappa coefficient 0.78) [4].

Data Quality

Data quality was ensured by using computerized assisted interviewing by trained nurse assessors, frequent teleconferences among the research nurses and project staff, and by fax-back and call-back of facility administrators. For the variables used in this analysis, data were missing data about for about 8% of data elements. Missing items were handled with mean-based single imputation. Single imputation is known to produce biased standard errors, but our inferences are based on relative differences in means and covariances among a set of QIs relative to validation scales between second and third generation QI adjustment procedures, and any bias introduced through this handling of missing data will affect both adjustment procedures equally.

Validation Model

As reported elsewhere [4], validation procedures for the original 21 QIs targeted by the Mega QI validation study involved expert clinical panel review of potential validation elements. Potential validation elements were categorized by quality of care construct along two dimensions: preventive and responsive. Preventive strategies represent the class of anticipatory actions that a prototypical good facility would engage in an attempt to minimize the emergence of problems (e.g., staff training and facility efforts at continuous quality improvement). Responsive strategies represent reactive actions a facility would engage in once a problem identified. By definition, facilities engaging in responsive strategies for a particular QI outcome should have higher QI scores than randomly selected facilities with lower responsive activity. Examples of responsive actions include documentation of comprehensive assessments, documentation of changes in resident status, and referrals to specialists [4].

A challenge to our secondary use of the Mega QI validation data is that our list of QIs includes more QIs than were covered in the data collection -representing refinement of the QI list by our group to improve the quality of the QIs and to extend their coverage. Many QIs may be affected by activities not captured. In addition, there has not been focused effort to decide which are appropriate preventive or responsive elements for the new and expanded set of QIs. To overcome this limitation, we identified 153 variables that were used as covariates in validation models for specific QIs summarized in the Mega QI validation report. Ninety-two (92) were responsive, 61 were preventive [4]. The included validation elements are described in an appendix to the Mega-QI final report (Mega QI Report Appendix F, accessible at http:// interrai.org/user_files/images/File/CMS%20%20-%20%20Final%20Report%20Appendix%20F%2012%20N OV%202002.pdf). We used principal components analysis (PCA) to extract 10 preventive and 10 responsive summary variables from among respective arrays of individual variables (i.e., the 10 first components from the PCA for each set). Within the preventive and responsive set, individual summary variables are uncorrelated (by design). Missing data among validation elements were handled with single imputation using the method of chained linear regression equations with hot deck replacement prior to the PCA procedure [22].

The determination of validity for the QI set is based on results of three separate regression models with the adjusted facility level QI score as the dependent variable.

Results

A limited number of representative characteristics of residents and facilities in the study are reported in Additional File 5. Overall, 19% of the sample are severely impaired in daily decision making, 29% are totally dependent in dressing, and 24% have depressive symptoms as measured by the Depression Rating Scale [23].

The third and second generation QIs had about the same magnitude of cross-sectional correlation with raw QIs (0.65 and 0.61, respectively). Autocorrelation (correlation of the QI with itself over one quarter) coefficients were about the same for third and second generation QIs (0.56 and 0.59, respectively, 95% CI on difference [-0.07, 0.00]); that is, based on a comparison of the magnitude of autocorrelation coefficients (Additional Files 6 and 7), the QIs scored with the two different algorithms cluster relatively evenly along the main diagonal. Selected QIs are labeled, including QIs with relatively high and low autocorrelation coefficients and QIs with large discrepancies between second and third generation risk adjustment procedures. Highly stable QIs are those where the underlying resident characteristic is relatively immutable (e.g., restraint use) while highly instable QIs reflecting rare or dynamic underlying resident characteristics (e.g., pressure ulcers). Some QIs in the same domain have very different stability coefficients (e.g., the presence of pain (PAI0X) is more stable than worsening pain (PAN01)).

Additional files 3 details -and additional file 4 summarizes -the results of the validation analysis. In evaluating validity, one more of the new QIs was in the Level-I (top) validity (n = 3) than were among the Mega QI set (n = 2) (detailed numerical results are available in Additional file 3). More than half of new QIs achieved a "Moderate" validation level (n = 56), including eight QIs that were considered "not valid" under the second generation adjustment methodology. Twenty-three (23) QIs were assigned to the "not validated" level.

Finally, we considered differences in the distribution of QI scores in different major care sectors. We display these results in an Additional file 8, which contains kernel density plots [24] for each QI cast as sparklines [25], and overlay functions for different political jurisdictions and clinical populations drawn form our multinational data base. Functions are shown for all facilities in our subsample (wide gray line), long term care (LTC) facilities in the Mega QI sample from six US states (thin gray line), Ontario, Canada LTC facilities (thin black line), Ontario rehabilitation facilities (tight dot line), Ontario complex continuing care hospitals/units (short dashed line) and Nova Scotia nursing homes (dash-dot line). This figure demonstrates the importance of choosing an appropriate reference population. Many QIs show a similar distribution across politico-clinical jurisdiction (e.g., ADL01), but others show distinct patterns (e.g., ADL06) and suggest the possibility of mixtures of facility types within jurisdiction. Therefore, it is important not only to consider how political factors (payment and reimbursement systems, and selection pressures for tertiary care centers) affect aggregate client populations and outcome statistics. The first panel of Additional file 8 shows that ADL06 (Proportion of residents who improve status on early-loss ADL functioning in dressing and personal hygiene, or remain completely independent in early-loss ADLs) is bimodal within the Ontario LTC sample. This could reflect different sub-groups of facilities within the Ontario LTC sample with fundamentally different distributions of residents being subject to the improve versus remain independent barrel of this double barrelled QI (meaning that the numerator is defined by two distinct events).

Discussion

A third generation adjustment algorithm for nursing home QIs returns reliable and valid estimates of nursing home quality of care. It incorporates stratification (matching within strata) and covariance adjustment. The new adjustment methodology can be used to score existing QIs and new QIs developed out of related instruments such as the interRAI Long Term Care Facility (interRAI LTCF; [26,27]) and CMS's proposed MDS 3.0 revision. The new method is not appreciably more computationally intensive than that of second generation QIs, in that essentially only two additional steps -stratification and then averaging across strata -are added to the adjustment procedure.

Although on the whole the third generation QIs had slightly lower (but not significantly lower) reliability estimates relative to second generation QIs, more of the third generation QIs were rated in the top validity levels. This counter intuitive finding might suggests that lack of reliability might reflect the occurrence and ability to detect true change for the new QI set, rather than poorer measurement. A high autocorrelation does not necessarily imply that evidence of validity will be found. We noted that the autocorrelation of pain worsening was greater than that of the proportion of residents in pain, but Additional file 4 shows that we were not able to provide validation for the pain worsening QI with the third generation adjustment method. However, the proportion of residents in pain did achieve a moderate level of validity (both second and third generation adjustment models).

A number (n = 23 of 79) of QIs we constructed were not validated in our secondary analysis of the Mega QI data. However, this signals only the absence of evidence of the validity of the QIs, not that the proposed QIs are not valid. Because Mega QI validation elements were collected via chart review and care giver interview for a select and focused set of QIs, requisite preventive or responsive care practices necessary to provide validity evidence for the expansive and broad set of QIs studied here may not be represented. Conversely, finding at least moderate validity for selected QIs may identify a set where positive evidence of preventive or responsive care practices recorded in the medical record or obtained by care giver interview. Such QIs may reflect, at least in part, between-facility variability in the way care is delivered and may be considered valid for public reporting, survey, or payment uses. Some of the new QIs tap care domains outside of the detailed chart review in the original CMS validation work. Therefore, our validity evidence likely provides a lower bound. In the future, we can examine alternative reliability estimates could have been generated, for example, using split resident population correlation coefficients. We do not have prospective validation data for these QIs.

Finally, it is important to note that some limitations of QI scoring in first and second generation frameworks remain in our proposed third generation framework. Particularly, the third generation framework does not directly address the problem of low base rates (rare events). More work is needed to address this issue. One approach would be to accumulate observations over a long period of time. Typically nursing home QIs are scored quarterly, but longer periods may allow the accumulation of more events and lead to more stable estimates. The cost of such an approach is the greater time lag may not capture current quality or care practices at a given facility. A better approach may be to limit consideration of viable QIs to those that do not have a low base rate.

Beyond the issue of validity, there are limitations in the conceptualization and operationalization of nursing home quality indicators that require additional research. With few exceptions, the first, second and our new third generation quality indicators are ultimately measures of aggregate clinical state or course of a facility's residents. As such, nursing home QIs reflect only one leg of the Donabedian structure-process-outcome model for quality of medical care [28]. Whether and how nursing home QIs are risk adjusted will not remedy this. A potential consequence of this framework is nihilism with respect to the prospects of developing and using measures of nursing home quality. Perhaps no better expression of the frustration of this state of affairs can be found in Charles Phillips et al.'s review, Where should Momma go? [29].

A limitation of our manuscript is that we do not present information comparing the relative proportion of facilities flagged as having poor quality under second and third generation adjustment procedures. Such an effort requires establishing a benchmark or threshold against which a facility may be judged as having poor quality of care. We are unaware of thresholds having been described for the first and second generation QIs, and they have not been described for third generation QIs. In the absence of clinically based thresholds, empirically based thresholds are typically used. For example basing judgments of poor quality on the basis of the mean, median or percentile of the adjusted QIs score. Use of empirically based thresholds would not be informative for comparing second and third generation QIs.

Conclusions

A main challenge of nursing home QIs is the issue of validity. Our view of validity includes well accepted notions of whether or not the measure adequately measures the intended construct, but extends into the use and interpretation of the statistic [30]. Unfortunately, there are no unambiguous gold standards for the assessment of nursing home quality. Rational choice of outcome domains and adjustment methods may add confidence to the derived measures, but only time and experience are capable of demonstrating the validity and usefulness of various methods for computing nursing home QIs.

Although some investigators have urged caution in over interpreting nursing home QIs [29], the validity of nursing home QIs and the potential malleability of nursing home QI scores has been demonstrated with a randomised controlled trials of a simple informational and educational interventions with the support of clinical consultation by a gerontological clinical nurse specialist [10].

Further work is needed on the development or QIs for policy purposes. Ultimately, the effective and unbiased use of QIs for quality reporting and payment systems should be preceded by significant development activities devoted to the problem of identifying appropriate reference facilities for individual facilities. One strategy could be further empirical work using facility-level matching algorithms. A second strategy might be the allowing facilities to nominate peer facilities. A procedure that merged the two would allow facilities to nominate matching facilities which were vetted as appropriate matches with empirically derived matching algorithms.

Additional material

Model 1 includes 10 orthogonal preventive components, Model 2 includes 10 orthogonal responsive elements, and Model 3 the 20 components together. Results of these models are used to classify QIs into levels of presumed validity following Morris and his colleagues [4]. The highest level of validity (Level I) is reserved for QIs where the multiple correlation coefficient from the preventive model (Model 1) is equal to or greater than 0.45 or from the combined model (Model 3) is greater than 0.55. The mid level of validity (Level II) is reserved for QIs not in Level 1 and where the multiple correlation coefficient from the preventive model is in the range [0.30, 0.45) or the from combined model is in the range (0.40, 0.55]. Remaining QIs are classed in Level III. Detailed results of these analyses are reported in Additional file 3, and summarized graphically in Additional File 4.
This article is available from: http://www.biomedcentral.com/1472-6963/10/96 © 2010 Jones et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. BMC Health Services Research 2010, 10:96
AcknowledgementsWork on this project was supported in part by a contract from the Canadian Institute for Health Information (JNM, Principal Investigator) and the National Institutes of Health (R01NR009071, P60AG008812Competing interestsThe authors declare that they have no competing interests.Authors' contributionsRNJ carried out the analysis and drafted the manuscript. JNM conceived of the analysis and participated in the direction and interpretation of the data analysis. BEF, JPH, JWP, MK and KB participated in the design of the study. All authors contributed to the writing and read and approved the final manuscript.Additional file 5 Table S1. Characteristics of Residents and Facilities.This file contains a table describing the characteristics of the residents and facilities used in the analysis. Additional file 6 Detailed numerical results from the reliability analysis. This file contains a table with the results of the reliability analysis (quarter to quarter autocorrelation) for the second and third generation quality indicators. Additional file 7Figure S1Scattergraph of autocorrelation coefficients for third and second generation quality indicators. This file contains a figure illustrating the correlation of autocorrelation coefficients for third and second generation quality indicators. Additional file 8 Density plots of each quality indicator. This document contains a density plot for each quality indicator for each major care sector.
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The habitus and technological practices of rural students: a case study

Laura Czerniewicz email@example.com
Centre for Higher Education Development
University of Cape Town
South Africa

Cheryl Brown
Centre for Educational Technology
University of Cape Town

The habitus and technological practices of rural students: a case study
A76021CA411BDB4FFBEDBC81548FC5E5Bourdieuethnographyhigher educationpracticesruralSouth Africastudentstechnologyuniversity
This paper describes the habitus and technological practices of a South African rural student in his first year at university.This student is one of five self-declared rural students, from a group of 23 first-years in four South African universities, whose access to, and use of, technologies in their learning and everyday lives was investigated in 2011 using a 'digital ethnography' approach.Their digital practices, in the form of their activities in context, were collected through multiple strategies in order to provide a nuanced description of the role of technologies in their lives.The student reported on here came from a school and a community with very little access to information and communication technologies (ICTs).While the adjustment to first year can be challenging for all students, the findings show that this can be especially acute for students from rural backgrounds.The study provides an analysis of one student's negotiation of a range of technologies six to nine months into his first year at university.Earlier theoretical concepts provide a lens for describing his practices through a consideration of his habitus, and access to and use of various forms of capitals in relation to the fields -especially that of higher education -in which he was situated.

Introduction

In 2011 we undertook a 'digital ethnography' (Murthy, 2008:1) of students' access to and use of technologies in their learning and everyday lives.During the analysis phase it became clear that it would be important to describe the challenges and adjustments students from rural backgrounds face, both dealing with the transition to university generally, and specifically adopting new technologies into their learning lives.This is not a unique local problem: research from other countries has shown that students from rural backgrounds face challenges succeeding in higher education.A study of various factors affecting students' successful completion at the Open University of Sri Lanka, for example, showed that 77% of rural students did not complete their degrees compared with the 44% of urban students who did not do so (Gamaathige & Dissanayake, 1999).Another study, from Australia, identified students from lower socioeconomic backgrounds living in rural areas as a distinct group at greater risk of educational disadvantage (James, 2002).

In South Africa, it has not been possible to obtain exact figures on the percentage of students from rural backgrounds in South African universities.This is made more difficult because students from rural backgrounds are often included either in the general category of 'disadvantaged students' or assumed to be in the group of students on financial aid.However, it is clear from the literature that rural schools continue to suffer poor, indeed worse, learning conditions on the whole compared with their urban counterparts.Motala, Dieltiens, Carrim, Kgobe, Moyo and Rembe (2007:52) note that despite improvements in funding equity, many learners, especially in the rural areas, continue to lack access to proper infrastructure and have to manage with limited text books, badly stocked school libraries and poorly trained educators.They also note that the single most powerful recommendation to emerge from the findings of the Ministerial Committee on Rural Education (Department of Education, 2003in Motala & Dieltiens, 2010) was the need to improve and equalise facilities and resources.In addition, learners in rural areas are less likely to attend school.Lewin (2009) analysed Demographic and Health Surveys from 25 Sub-Saharan Africa (SSA) countries and found that urban children have about four times more chance of being enrolled in Grade nine than rural children in the data set.Studies of educational exclusion make the same finding -that more out-of-school children are to be found in rural areas than are to be found in urban areas (Motala & Dieltiens, 2010).

There is also evidence that students in rural schools achieve worse results than their urban counterparts.Motala et al. (2007) observe that learners who fared less well in the Department of Education's national assessments of learning achievement for Grades 3 and 6 came from (in descending order) township, farm, rural and remote rural schools.Many Quintile one schools are in rural areas, with the Free State having 1 64.1% of Quintile one schools in South Africa, of relevance because four of this study's five rural students (including the student reported on in this paper) were from the Free State.The concerns about these schools and their general poor situation have been addressed in a number of ways, one of which is inclusion in the 500+ Dinaledi schools that receive National Treasury grants to improve mathematics and science results.

The statistics reveal that only 7.7% of the learners in Quintile one schools who wrote the National Senior Certificate exams in 2009 passed (Financial & Fiscal Commission, 2011).This means that the demographic and geographical spread of the country's students is not proportionately represented in universities.One of the few studies which consider rural students in universities (Tumbo, Couper & Hugo, 2009) reviewed lists of undergraduate students admitted from 1999 to 2002 in Health Science faculties, in terms of whether they had rural, town or city postal codes.They found that 59% of the students were from cities, 15% from towns and 26% from rural areas.They concluded that the proportion of rural-origin students in medical studies at that time in South Africa was considerably lower than the national rural population ratio of 46.3% (and of course disproportionate to the number of medical professionals in rural areas).

While there are numerous studies about the transition to university of disadvantaged students (as defined economically), there appear to be few that focus specifically on those from rural areas, and those specifically from poor rural areas.Kapp and Bangeni (2011) describe some of the challenges students from educationally disadvantaged backgrounds (many from rural areas) face in negotiating aspects of academic literacy at university.They show how students encounter an essentially foreign culture at university, and have to reconcile conflicting transitional spaces of their home and university identities.We have found no studies specifically addressing university students from rural backgrounds in terms of their technological literacies and associated issues.Information from a non-governmental organisation (NGO) supporting rural students in higher education says that in student feedback reports on their experiences at university "the unfamiliarity of technology comes up again and again as a real challenge for new students" (Glover, pers comm., 2012).This is not surprising given that Internet penetration is only 4.6% in South African rural areas compared to 21.8% in urban areas (Pejovic, Johnson, Zheleva, Belding, Parks & Van Stam, 2012).Although mobile telephony is decreasing the location-based divide, the disparity between rural and urban youth is still pronounced; while 70% of urban youth over 16 use mobile phones, only 49% of their rural counterparts do so (United Nations Children's Fund (UNICEF) New York, Division of Communication, Social and Civic Media Section, Beger G & Sinha, 2012).

In a study on student dropouts, Brits, Hendrich, Van der Walt and Naidu (2011) review work reported on by the Rural Education and Access Program (REAP) in 2008 and 2010 which states that a rural background may have a negative impact on student success because students from disadvantaged backgrounds are usually underprepared for tertiary education, but at the same time the preparedness of the institutions to accommodate underprepared students is sometimes questionable (REAP, 2008:6, in Brits et al., 2011).In their colloquium report of 2010, REAP reports that rural students' success is constrained by financial factors including the fact that many cannot afford to go home for their holidays; academic factors which see them confront the fact that although they have passed Grade 12 they lack the necessary competencies required for tertiary study; and socio-cultural factors, including isolation and alienation in a new environment.In this paper we therefore focus on the story of one student, from what, by his account, is a seriously marginalised context.Through a narrative approach we explore how a student from a rural background navigated the use of technologies in his first year at university and we challenge assumptions that are made about the position of technology in the lives of students from disadvantaged backgrounds.We also explore the learning pathways this student followed in acquiring ICT-based skills needed to successfully traverse higher education.

Theoretical framework

Students' technology practices cannot be described in isolation; these are not neutral activities.We find Bourdieu's (1984Bourdieu's ( , 1986;;Bourdieu & Wacquant, 1992) framework a valuable way to describe student practices in the context of the field in which they occur, and in terms of their habitus and the capitals they are able to bring to bear to their practices.Bourdieu (1984:101) summarises his framework as "[(habitus) (capital)] + field = practice".A field is a distinct social space consisting of interrelated and vertically differentiated positions, a "network, or configuration of objective relations between positions" (Bourdieu & Wacquant, 1992:97).In the case of this study, we understand higher education to be a distinct social field, and we also understand the rural community in which the students live to constitute a different social field.Bourdieu (1986) describes four forms of capital: economic, social, cultural and symbolic.Economic capital refers to assets either in the form of, or convertible to cash.Social capital is "the sum of the resources, actual or virtual, that accrue to an individual or a group by virtue of possessing a durable network of more or less institutionalised relationships of mutual acquaintance and recognition" (Bourdieu & Wacquant, 1992:119).Cultural capital occurs in three states: embodied, objectified, and institutionalised.Jenkins (2002) explains that embodied cultural capital refers to long-lasting dispositions of the mind and body, expressed commonly as skills, competencies, knowledge and representation of self image.Objectified cultural capital refers to physical objects as "cultural goods which are the trace or realization of theories or critiques of these theories" (Bourdieu, 1986:243, mentions pictures, books, dictionaries, instruments, machines).Institutional cultural capital is the formal recognition of knowledge usually in the form of educational qualifications.Symbolic capital is appropriated when one of the other capitals is converted to prestige, honour, reputation, fame -it is about recognition, value and status.

Habitus has been widely used in education, specifically to consider the way that disadvantaged students mediate their educational experiences.Bourdieu (1977:vii) described habitus as "a system of durable, transposable dispositions which functions as the generative basis of structured, objectively unified practices".This complex definition has been explained to mean that a habitus lens provides a way of showing students' "ways of acting, feeling, thinking and being…how [they] carry [their] history, how [they] bring this history into [their] present circumstances, and how [they] then make choices to act in certain ways and then not others" (Maton, 2008:53).This concept has been critiqued by many scholars as being determinist and leaving little room for agency.In a detailed review of these debates, Mills (2008) argues that the generative dimension of the concept can indicate transformation as well as reproduction.Therefore while an individual's habitus -inculcated by everyday experiences within the family, the peer group and the school -disposes actors to do certain things, orienting their actions and inclinations, it does not determine that they do so.As Mills (2008) points out, Bourdieu explained that habitus is a strategy-generating principle enabling agents to cope with unforeseen and ever-changing situations.The point is that there is no such thing as pure agency; but a kind of (limited) agency can be identified.She makes the point that those with a more transformative habitus recognise opportunities for improvisation and act in ways to transform situations.Thus, she observes that what one may experience as incapacitating, another may see as generative of opportunities for self-enhancement or self-renewal.

As this is not a study of agency, but one of practices, the specific issues of agency -both empirical and theoretical -do not receive attention.Rather the focus is on practices, especially the habitus and the ways that various capitals are drawn on and exerted in the fields, particularly that of higher education.

Methodology

This paper describes the habitus and technological practices of a South African rural student in his first year at university.It arises from a project which used a 'digital ethnography' (Murthy, 2008) approach to study 23 first-year students in four South African universities (Cape Town, Fort Hare, Free State, and Rhodes universities) during 2011.Digital or virtual ethnographies have been used to expand opportunities for data collection beyond the physical (Murthy, 2008) and to enable researchers to better understand people's technological culture (Hine, 2008).We drew on Ethnographic Action Research (EAR) as a methodological framework as it combined participatory techniques and ethnographic approaches into an action research framework (Tacchi, Foth & Hearn, 2009).Consequently we recruited and trained researchers located at each study site.The on-site researchers were embedded in the context of the student participants thus available and able to establish a relationship with them.This methodology allowed for the dynamic interchange between researcher and participant; it acknowledged that each influences the other -thus the inclusion of Action Research within its framework.The sources of data for the project (and this paper) comprise a range of interviews (transcribed and viewed), video records and transcripts, transcripts of a focus group as well as digital diaries and social media (Facebook) observations.

The student described in this paper is one of five self-declared rural students from a group.When it became evident that the rural students were of particular interest, we realised that we would lose the richness of their narratives if we tried to focus on all of them.We selected Jake (a pseudonym) purposefully.He had engaged consistently throughout the project and provided an extensive mix of interview and digital data.He had also responded by email when clarification on data and findings was sought.

This paper takes the form of a descriptive case study because it seeks to examine a particular phenomenon, viz. the assimilation of technology into the learning life of a rural student in a real-life context, namely, the first year of university (Yin, 2003).In examining this particular event in a real-life context we are looking at a bounded system in its own habitat (Stake, 1978).We acknowledge that this is one person's story and consider it important through the insight it offers into the challenges of a marginalised group of students in higher education.

Data analysis was framed by the theoretical lens and the concepts of Bourdieu's (1986) framework -habitus (with indicators of family background, schooling, and reported competence), economic capital, cultural capital (specifically embodied and objectified), social capital and field.

Transcribed interview evidence for all participants formed the basis of a content analysis that developed four coding matrices based on the research framework.The first three were for the past, current and future use of ICT by students.The fourth defined their use of varied aspects of social media.These were combined into a highly structured coding matrix using NVivo software which was used to code all the students' interviews.In addition, further analysis of all the data sources was undertaken for selected students' cases.In the case of Jake, we also sought clarity on further issues via email, and gave him the opportunity to read the draft of this paper.Whilst we sought informed consent from all students for the research and guaranteed their anonymity in publication, it was important that an individual student view our academic interpretation of his story.

Jake's story

Habitus is a way of describing how, "amongst other things [students], carry [their] history, how [they] bring this history into [their] present circumstances" (Maton, 2008:53).Therefore Jake's personal background is relevant to understanding his later digital practices.

Background

Jake, aged 19, is a first-year Humanities student, the first person in his family to go to university.He is trilingual, speaking seSotho as a home language but also fluent in isiZulu and English.His mother, who worked in town as a factory worker, had died when he was 12 years old and he described his father as a self-employed tiler.His father had two wives and four children, all of whom Jake considered his siblings (he spoke of his sibling on his mother's side, and his siblings on his father's side).Neither of his parents successfully completed their schooling; his mother left before the end of her school years and his father failed the final school exams.Jake is the first person in his family to successfully finish school and be accepted for university.

Jake attended primary and high school in small rural schools, the primary school having fewer than 100 pupils, the high school under 500 pupils.His high school was one of the afore-mentioned Dinaledi schools focusing on mathematics and science.Because of the additional grant these schools received, his school did have some technology, but as noted below, he did not have access to it.

Economic capital and access to technology

Jake's access to the types of capital which he could draw on in the field of higher education was limited.In terms of economic capital, there was a substantial shift when he got a university bursary that paid for his laptop and smartphone.Before he came to university he had had few encounters with technology.His school had two computers in the library but he had never touched them.He got his first cell phone when he was in Grade 10, and until he came to university he thought a cell phone was the most important item of technology to have.

He says that when people in his village need to access the Internet from a computer they have to travel to an Internet cafe and that "it is costly".He makes a similar observation about accessing the Internet from cell phones when he notes that, "Most of my peers had cell phones that had internet but they couldn't use them to surf the net due to coverage issues and costs".He says he now uses the Internet more than his friends at home because, "when I get to the internet, already having like paid for the data services and stuff, I get for free.So to them it is still like they have to budget for the whole thing, so I think it is more accessible to me than it is to them."It is through the economic capital Jake acquired when winning the bursary that he gained access to what he describes as the "individualism" of his own laptop.He says that the university does have computer labs but that he prefers having his own machine, which he points out he uses differently.Having the luxury of an individual machine means that "there are no monitors" and that while in labs, "there are other students and you have to be silent".He notes that, "with your own computer you can angle it the way you want and watch whatever you like".

Having the money to buy a laptop and phone also meant that he gave away other devices such as flash drives and memory sticks to others "at home, for music and stuff".

If considered in terms of his university context, his economic capital or resources are mixed.On the one hand Jake's computer at six months old is considered relatively old, and he describes it as, "problematic.Full of matata, problems.This Acer of mine is now becoming too old".But Jake is mindful of the fact that he is on a bursary and says that he would never ask his family for money to buy technology as he cannot justify it to them; he points out that they would not understand what he was asking for.He also adds that he still has credit on his bursary, so this is another reason not to ask his parents.

While the bursary has literally provided Jake with economic resources he did not previously have, he continues to operate in an environment where technology practices are shaped by resource constraints.Thus he does all his printing in the university labs, as he does not have a printer.And he notes in one of his Facebook status updates that:

It ws a shock @ first 2 notice dat many of my frnds r no longer updating anythng on fb, bt then again I got reminded of da fact dat skuls r closed.Whch minz no more easy access 2 free computers on campus.Only dose wit smartphnes r beatin da recession.# shame #.Jake is aware of the privilege his financial position offers him and that without access to centralised university resources such as computer labs, his fellow students are cut off from their online social networks.

Social capital

Acquiring a computer has given Jake access to social capital and changed the dynamics of his relationships.

He is able to help his room-mate as he is the one who has the computer.Jake shares his laptop with his room-mate when he needs the Internet, otherwise he would have to travel a distance to gain access.Jake says: I'm logging in…to send an SMS.My room-mate's mother.Ja, [he] is looking for money, money.…My room-mate wants to send an SMS, let me just login.My roommate is going to send an SMS to his Mama.Apparently, he's looking for something like money, whatsoever, so I am going to make space for him.He has gained new networks, and he calls on them for help if he has problems with his computer, particularly his network of neighbours in the hostels.He says that he has never asked for help in the computer labs; even though there are plenty of people there.He says it takes too long for anyone to respond if he raises his hand, observing that "they don't even see you".

His online networks have changed substantially in the six months that he has had the laptop and the Blackberry, as he did not have a computer before (and nor did anyone in his family) and his previous cell phone did not have easy Internet connectivity.

He uses Facebook on the computer regularly (at least every hour) and both Facebook and Mxit on the Blackberry.He has between 350 and 400 Facebook "friends", of whom he says only 15 are friends known to him personally.The networks are through his church, his home community and through his connections in another province.He is very active on lists and groups, many of them related to his church and other religious groupings.He says that Mxit is largely for a small group of his church friends.

Cultural capital

Jake is aware that before he came to university he had limited cultural capital in terms of computer literacy, and he observes that although there were in fact two computers at the school he did not use them because he did not know how "to compute or surf the net.In our school we didn't have computer literacy skills."

He notes that "the technological infrastructure in my home comprised only a radio".He says, "In our community no one has a computer" and he adds that the only technology that matters in his home village is "music systems".He does not link the lack of technology to lack of finance or economic capital, but interestingly, rather to cultural capital.Thus, he says, "In my community technology is not that important as people lack the skills to use it."

"Before I came to varsity technology was important as I understood its vitality (sic) in the Information Economy we are living in.I always dreamt of owning a laptop."As soon as he got to university, he did a first-year basic computer literacy course for six months.He said these months were tough and that he failed the first test and needed help with the practicals.He continues to use the computers in the lab, as they have software programs that he does not have on his laptop -he is struggling to finish a PowerPoint and Excel installation, for example.Jake is expressive when he explains that:

My rural background was very challenging upon my arrival at the UFS.The computer literacy module, BRS 111, was like rocket science to me.To top it up, lecturers had started making use of Blackboard for notifications.It was a real challenge.Typing assignments in accordance with the required formats was even more threatening.Universities are congested with computers and it is unwittingly assumed that all students can use those computers beneficially; which is not the case.Rural students feel estranged and depressed by these technologies.Setting appointments via email, checking emails from the varsity, and doing research for assignments are scary activities to rural students.He is proud of what he has achieved, and says, "it is becoming easier and easier as I get familiar more with the technology… .Now I'm like accustomed to everything and I know the functions, ja.Now, I love it."

Objectified cultural capital

The importance of both the computer and the Blackberry in Jake's life is captured by two of his Facebook statuses.The first reads:

"Imagine life without this tiny device called a Blackberry.Imagine life without this cute portable thing called a laptop...jerrrr.....tasteless."The second reads:

"nna to be honest these two r rulin my world..of course dey come after my beautiful galfriend."For Jake, his computer is a valuable artefact: "I don't want to play games on my laptop, as the heavy use of keys may damage my keyboard."It is an important part of his life now, as a student.He says, "The thing is that at Varsity level you can't survive if you do not have your own laptop.Cos' like, truly speaking, we have computer labs, so before I bought my computer, I still had access to computers, but I just felt that I needed my own."He estimates that he spends 80 hours on his laptop a week, guessing that over half of that is on "academics" with the rest being entertainment, particularly music.Of his six-month journey, Jake says, "I know my way around the computer everything is satisfactory, it is how we live now."For Jake, the laptop is about his studies and his music: "So, my laptop, it has to do its job; assist me academically and entertain me.I can't just put it away and not play on it, then it's not doing its job."

Yet, while Jake prizes his computer, he believes his cell phone is indispensable.He says, "I can't imagine life without my cell phone" -it is always with him and he never switches it off.He would find being in an area without cell phone reception problematic.He says that he will answer texts at the dinner table and that his concession to university lectures is to log out of Facebook and Mxit on the phone, although I jst cnt connect with Twitter.Its so shortcut n complex.@ Jake.@ Kido...@ Nomvie…such titles n da scribblings confuse me.He also says, "So, Twitter is more competitive than Facebook, Facebook is how you feel."

It is interesting that Jake perceives his laptop to be part of the academic world he has entered, an aspirational part of the information economy, and also how closely he links expertise, skills and training with the computer as specific type of technology.

Field

Jake sees the challenges he has experienced with technology as part of the wider sweep of challenges that he has had to deal with in the transition to university.He is aware of the dramatic effect that this can have and he observes that not only for him, but also for others, these transitions can be a shock:

Since campuses appear to be sophisticated, rural students prefer to abstain from exploring them with the fear of their rural mentality being exposed.Apart from technology, lecturers make it tough for rural students because these lecturers are used to Model C schools and the way those schools teach.As a result, their tone suits private school graduates.Their language and examples are in line with these urban students.No one seems to understand township or homeland students.His habitus can be described as transformative both in terms of his experiences at university and at home.In both cases there have been changes.His relationship with his class mates has changed as it has with his home community.He says that while at university he knows less than his friends, at home he is "the mastermind".Being ICT literate gives him a kind of freedom, "And I think that I am the only computer literate one in my family.So, I am free to do anything I want, because they don't understand what it is."Fellow students ask if his family know what Facebook is and laugh; Jake replies, "They do not understand."

His friends, family, relatives and teachers back home think he is "clever with technology" and they ask his advice about things, for example, about "how a webcam functions".He is the first person in his family to own a laptop.

He comments that while he is connected to his university lecturers and emails them, he is not in contact with teachers from his school as they are "not that much computer literate".For example he says that they hand work to the school clerks to type for them, "to do reports and stuff".With regards to his friends at home, he says he largely uses Short Message Service (SMS), that he cannot Blackberry Messenger (BBM) them as many of them do not own Blackberry cell phones.When he returns from a short break at home he notes, "At home I did not use any technology, at all -Kwala."

There is one area of continuity, that of music.He had noted that the only technology that matters in his home village is "music systems".Music continues to be very important to him."The first thing I do always is play music [on my computer]; …But everything I do, I do to music."This is the one aspect of his new computer that he values highly, demonstrating in the recorded videos, how his Virtual disc jockey (DJ) system works.He is clearly proud of the expertise that he has gained in this arena.

Conclusion

While this paper focuses on students' technological habitus, it also demonstrates that technological literacies are interwoven with other literacies as only one component in a dense skein of experiences and adjustments for first year students.Bourdieu (1986) provides a useful conceptual lens for describing how one student uses and engages with technology, as well as framing the way his background shapes his response to his new environment.The findings highlight an example of a transformative habitus -as Jake himself says in relation to his background: "I'm not sorry of myself, despite my regrettable background.Actually, it's that background which keeps reminding me that I have to work even harder to stay competitive."Jake's ability to develop symbolic capital through the motivation his background has elicited in him thus provides an additional thread that runs through his story.

Jake's story serves as a stark reminder that for students from disadvantaged backgrounds -in this case from a rural background -access to success in higher education is more than just entrance to university.It is about on-going and continuing access to various forms of capital such as financial assistance and the establishment of new support networks.It is about the disjuncture between background and institutional culture, and the consequent reforming of individual habitus in the light of the imperatives of the higher education terrain.In negotiating this path, technology has played both an inhibiting and enabling role for Jake.While it was initially yet another challenge to navigate, it proved to be an enabler to building his confidence, establishing new connections and providing new opportunities for him to pursue his passions.The multiple forms of data provided by the 'digital ethnography' methodology of this study afforded a lens to Jake's technology practices and provided a view of his strategies for success.

Note
 The poorest schools are included in Quintile one and the least poor in Quintile five. Schools are classified first by a national poverty table, prepared by the Treasury, that determines the poverty ranking of areas. It is based on data from the national census including income levels, dependency ratios and literacy rates in the area. Secondly the provinces then rank schools from Quintile one to five, according to the catchment area of the school. Each national quintile contains
20% of all learners, with Quintile one representing the poorest 20% and Quintile five the wealthiest 20%. However, provincial inequalities mean that these quintiles are unevenly distributed across provinces.
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'Someone posted on your wall', 'Someone has sent you an email' or something like that and you have to go and check it out.So, I decided to cut off my Blackberry Internet Service (BIS) and I did not renew it for this month.I will only like renew it when we go to recess, because of these like tests and stuff, ja.At the moment I only access Facebook when I am in my room at night.Digital practicesIt is evident from his self-recording that Jake uses his two devices in a variety of ways, and that he multitasks.He moves between devices, for example when he writes on a friend's Facebook page, "Hey monna inbox me ur mxit..." in order to have a private conversation.It is also clear that he uses social media for both academic and personal purposes, an indication of the kind of blurring that happens with both software and hardware.While most of his Facebook statuses (and comments on his friends' statuses) are personal (including many quotes from popular songs and religious references) they also include affective comments and reflections including: Study hard n al shall follow.Stop whining n study.Good luck 4 da second semester.I so wish textbooks were designed lyk magazines.Ten years frm nw u probably wud stl rememba da story of Kelly Khumalo in Drum Magazine, whch minz evn chapters wud last 4eva in one's mynd.[names a friend] I'm studyin nw.Procrastination is a rapist of tym.He is on video record using his Blackberry as a modem and emailing his work to other students with whom he will be working in the lab.In one clip he is using Mxit on his Blackberry, Facebook on his computer, and is also playing music.He thinks about technology and notes in one of his Facebook statuses that he does not like Twitter:
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