Interventions to reduce adverse health outcomes resulting from manifestations of gender bias amongst immigrant populations: a scoping review
Bibliographic record
Abstract
BACKGROUND: Immigrants to Western countries increasingly originate from countries with pervasive gender inequalities, where women experience disproportionately high rates of threats to their well-being. Health and social services in countries of settlement encounter several adverse outcomes linked to gender bias among immigrant groups. Little is known about interventions implemented to address manifestations of gender bias among immigrant populations. METHODS: A scoping review was undertaken to describe the literature on existing interventions and determine knowledge gaps. Nine academic and grey literature databases were searched for literature, with four reviewers screening the results. RESULTS: Of the 29 included reports, most targeted domestic violence amongst the Latino population in the United States, with few interventions focusing on other outcomes, populations, and settings. The majority reported achieving their objective, although 13 interventions were not evaluated. CONCLUSIONS: Future research and practice to address gender bias among immigrants may benefit from expanding on ethnic diversity, designing and reporting evaluations, addressing the context of gender inequities, tailoring to local community needs, and engaging community-based groups.
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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 | 0.013 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".