<|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:"Implementation of a SenseMaker® research project among Syrian refugees in Lebanon."
Abstract:"The Syrian conflict has displaced over 1.2 million Syrians into Lebanon. As a result of displacement, some Syrian families are turning to child marriage as a coping mechanism. The prevalence of early marriage has reportedly increased and the average age of marriage decreased during the crisis. The aim of the project was to understand the underlying factors contributing to child marriage among Syrian refugees in Lebanon using Cognitive Edge's SenseMaker((R)). This manuscript explores the process of implementing this novel research tool in a humanitarian setting. Twelve interviewers conducted SenseMaker((R)) interviews with married and unmarried Syrian girls, Syrian parents, as well as married and unmarried men. Participants were asked to share a story about the lives of Syrian girls in Lebanon and to self-interpret the narratives by answering follow-up questions in relation to the story provided. Data collection occurred across three locations: Beirut, Beqaa, and Tripoli. In total 1422 narratives from 1346 unique participants were collected over 7 weeks. Data collection using SenseMaker((R)) was efficient, capable of electronically capturing a large volume of quantitative and qualitative data. SenseMaker((R)) limitations from a research perspective include lack of skip logic and inability to adjust font size on the iOS app. SenseMaker((R)) was an efficient mixed methods data collection tool that was well received by participants in a refugee setting in Lebanon. The utility of SenseMaker((R)) for research could be improved by adding skip logic and by being able to adjust font size on the iOS app.."
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 = [4,7];
Q3 = [2];
Q4 = [0];
Q5 = [];
Q6 = [1].
Implementation of a SenseMaker® research project among Syrian refugees in Lebanon
The Syrian conflict has displaced over 1.2 million Syrians into Lebanon. As a result of displacement, some Syrian families are turning to child marriage as a coping mechanism. The prevalence of early marriage has reportedly increased and the average age of marriage decreased during the crisis. The aim of the project was to understand the underlying factors contributing to child marriage among Syrian refugees in Lebanon using Cognitive Edge's SenseMaker((R)). This manuscript explores the process of implementing this novel research tool in a humanitarian setting. Twelve interviewers conducted SenseMaker((R)) interviews with married and unmarried Syrian girls, Syrian parents, as well as married and unmarried men. Participants were asked to share a story about the lives of Syrian girls in Lebanon and to self-interpret the narratives by answering follow-up questions in relation to the story provided. Data collection occurred across three locations: Beirut, Beqaa, and Tripoli. In total 1422 narratives from 1346 unique participants were collected over 7 weeks. Data collection using SenseMaker((R)) was efficient, capable of electronically capturing a large volume of quantitative and qualitative data. SenseMaker((R)) limitations from a research perspective include lack of skip logic and inability to adjust font size on the iOS app. SenseMaker((R)) was an efficient mixed methods data collection tool that was well received by participants in a refugee setting in Lebanon. The utility of SenseMaker((R)) for research could be improved by adding skip logic and by being able to adjust font size on the iOS app.