<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are an expert on climate change, human mobility and migration. Answer the question truthfully.<|eot_id|><|start_header_id|>user<|end_header_id|>Here are the title and abstract of a scientific paper probably related to climate change, human mobility and migration..
Title:"Modeling the predictors of mobile health adoption by Rohingya Refugees in Bangladesh: An extension of UTAUT2 using combined SEM-Neural network approach."
Abstract:"While the healthcare facilities for the people is questionable in Bangladesh, Rohingya refugees is a burning issue for both Bangladesh and global community. Integrating Rohingya refugees into the framework of mHealth could be beneficial for both Bangladesh and Rohingya refugees in general, and in specific situation like COVID-19 outbreak. However, no research has been found on what motivates Rohingya refugees to accept mHealth in Bangladesh. Drawing on the UTAUT2 model, this study investigates the predictors of acceptance of mHealth services technologies among Rohingya refugees. The study also seeks to clarify the roles of mHealth developers, the Bangladesh government, and non-governmental organizations working with the 1.1 million Rohingya refugees in Bangladesh. Quantitative data were collected from refugee camps with the permission of the Refugee Relief and Repatriation Commissioner (RRRC). The data were analyzed in two stages using a mixed approach that combines PLS-SEM and Artificial Neural Network (ANN). This study revealed that Effort expectancy (EE, with t = 5.629, & beta; = 0.313) and facilitating conditions (FC with t = 4.442, & beta; = 0.269) in PLS-SEM, and FC (with 100 percent importance) and Health consciousness (HC, with 94.88 percent importance) in ANN analysis were found to be the most substantial predictors of mHealth adoption. The study also revealed that EE and FC are more important for low education group, while PE and Situational Constraint (SC) are more important for the high education group of refugees. In addition to providing insights for mHealth developers, this study particularly focuses on the role of government institutions and non-governmental social workers in working with the subjects to increase FC and HC among Rohingya refugees and bring them under mHealth services.."
I have six questions you need to answer.
Q1: Is this article is about 'human migration mobility': 1 = yes; 0 = no. Answer with a number. Do not give a textual explanation.

Q2: What drivers of migration and mobility does the article study? 0 = 'other', 1 = 'climate', 2 = 'environment', 3 = 'economic', 4 = 'conflict', 5 = 'political', 6 = 'social', 7 = 'family'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q3: What is the overall sentiment of the article?:0 = 'neutral', 1 = 'negative', 2 = 'positive'. Answer with a number. Do not give a textual explanation.

Q4: What type of climate or environmental keywords are explicitly mentioned?:  0 = 'none', 1 = 'algal blooms', 2 = 'infestation', 3 = 'infectious diseases', 4 = 'ocean acidification', 5 = 'sea ice', 6 = 'acid rain', 7 = 'drought', 8 = 'cold wave', 9 = 'heat wave', 10 = 'glacial', 11 = 'wildfire', 12 = 'air pollution', 13 = 'biodiversity loss', 14 = 'coastal erosion', 15 = 'land degradation', 16 = 'deforestation', 17 = 'desertification', 18 = 'water pollution', 19 = 'diebacks', 20 = 'permafrost loss', 21 = 'sand mining', 22 = 'sea level rise', 23 = 'earthquake', 24 = 'tsunami', 25 = 'volcanic', 26 = 'flood', 27 = 'avalanche', 28 = 'landslide', 29 = 'downburst', 30 = 'thunderstorm', 31 = 'sandstorm', 32 = 'haze', 33 = 'storm', 34 = 'cyclone', 35 = 'blizzard', 36 = 'hail', 37 = 'snow', 38 = 'tornado', 39 = 'wind', 40 = 'hurricane', 41 = 'typhoon', 42 = 'other'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q5: What discipline does the article belong to?: 0 = 'other', 1 = 'social science', 2 = 'natural science', 3 = 'arts and humanities', 4 = 'multidiscipline and interdiscipline'. Answer with a number. Do not give a textual explanation.

Q6: Does the article apply quantitative and/or statistical analysis?: 0 = 'no', 1 = 'yes'. Answer with a number. Do not give a textual explanation.

Use the following template: 

Q1 = [];
Q2 = [];
Q3 = [];
Q4 = [];
Q5 = [];
Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
Q1 = [1];
Q2 = [4];
Q3 = [];
Q4 = [];
Q5 = [1];
Q6 = [1].
Modeling the predictors of mobile health adoption by Rohingya Refugees in Bangladesh: An extension of UTAUT2 using combined SEM-Neural network approach
While the healthcare facilities for the people is questionable in Bangladesh, Rohingya refugees is a burning issue for both Bangladesh and global community. Integrating Rohingya refugees into the framework of mHealth could be beneficial for both Bangladesh and Rohingya refugees in general, and in specific situation like COVID-19 outbreak. However, no research has been found on what motivates Rohingya refugees to accept mHealth in Bangladesh. Drawing on the UTAUT2 model, this study investigates the predictors of acceptance of mHealth services technologies among Rohingya refugees. The study also seeks to clarify the roles of mHealth developers, the Bangladesh government, and non-governmental organizations working with the 1.1 million Rohingya refugees in Bangladesh. Quantitative data were collected from refugee camps with the permission of the Refugee Relief and Repatriation Commissioner (RRRC). The data were analyzed in two stages using a mixed approach that combines PLS-SEM and Artificial Neural Network (ANN). This study revealed that Effort expectancy (EE, with t = 5.629, & beta; = 0.313) and facilitating conditions (FC with t = 4.442, & beta; = 0.269) in PLS-SEM, and FC (with 100 percent importance) and Health consciousness (HC, with 94.88 percent importance) in ANN analysis were found to be the most substantial predictors of mHealth adoption. The study also revealed that EE and FC are more important for low education group, while PE and Situational Constraint (SC) are more important for the high education group of refugees. In addition to providing insights for mHealth developers, this study particularly focuses on the role of government institutions and non-governmental social workers in working with the subjects to increase FC and HC among Rohingya refugees and bring them under mHealth services.