Predictors, barriers, and facilitators to refugee women’s employment and economic inclusion: A mixed methods systematic review
Bibliographic record
Abstract
Refugee women's employment and economic inclusion have emerged as significant areas of focus, with these women facing unique challenges due to their gender, refugee status, and sociocultural identities. Policymakers and researchers worldwide are giving this issue increased attention. This systematic review uses a mixed methods approach and includes 31 studies to explore the predictors, barriers, and facilitators of refugee women's employment. The results reveal a pooled employment rate of 31.1% among refugee women. It identifies demographic features, language proficiency, education, and family structure as critical determinants of employment. The qualitative synthesis uncovers three key themes: the meaning and significance of employment; barriers to employment; and facilitators and coping for employment. This study underscores the multifaceted influences on refugee women's employment. The findings can inform the creation of more targeted interventions, policies, and practices to support refugee women's employment and economic integration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".