Does volunteering impact refugee women's life satisfaction, empowerment, and wellbeing? Experimental evidence, local knowledge, and causal reasoning
Bibliographic record
Abstract
BACKGROUND: There are gaps in the evidence base addressing whether volunteering programs enhance the wellbeing, empowerment, and life satisfaction of individual volunteers. Program impacts are seldom rigorously evaluated, whilst construct meanings remain largely unspecified, especially in the Middle East. This study tested the impacts of We Love Reading, a program training volunteers to read aloud in their local communities. It also mapped local knowledge representation. METHODS: We conducted a mixed-method program evaluation based on a randomized cluster trial with 105 Syrian refugee women from poor households in Amman, Jordan. At three time points (baseline, 5-month and 12-month-follow-up), we implemented a survey to measure levels of life satisfaction (Cantril), psychological empowerment (PE), and psychological wellbeing (PWB). We used regression models on panel data to estimate individual-level impacts, adjusting for women's characteristics and the moderating effects of their social networks. We also conducted net-mapping sessions to clarify local concepts and their causal connections, generating thematic analyses and fuzzy cognitive maps (FCMs) to represent local knowledge and causal influences. RESULTS: Life satisfaction was the only outcome variable showing a significant impact for We Love Reading (Cantril, β = 3.00, p = 0.002). Thematic analyses and FCMs made explicit the multi-dimensional aspects of lived experiences: emphasis was placed on reaching goals, having "the full right to act," the freedom to take decisions, willingness and determination. Women explained that building their empowerment and agency was a main driver of life satisfaction, and that volunteering boosted the resolve of "not giving up" on life goals. CONCLUSION: This program evaluation integrates scientifically-rigorous and culturally-relevant methodologies to identify impacts, local knowledge systems, and causal pathways of influence. This helps clarify how and why volunteering works in real-life situations across cultural contexts, calling attention to what programs seek to achieve, how they avoid volunteer burden, and why they generate social change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".