Gender differences of health literacy in first and second generation migrants: A systematic review
Bibliographic record
Abstract
Abstract Background Health literacy, defined as all skills and capabilities that enable a person to access, understand, appraise and apply health information, is a key factor regarding disease management, health outcomes, and health-decision-making. Internationally, migrants have been identified as a high-risk group for limited health literacy. However, it remains unclear if female and male migrants process health information differently. This systematic review aims to analyze gender differences in the health literacy of first and second generation migrants. Methods We performed a systematic review according to PRISMA guidelines. We searched OVID (MEDLINE), PsychInfo and CINAHL for original articles providing extractable data on the health literacy of male and/or female migrants. Two reviewers independently reviewed abstracts and full text articles for according to predefined inclusion criteria, including the use of a validated health literacy measurement tool, applying it to first and/or second generation adult migrants. We adapted a data extraction sheet from the Cochrane Collaboration for extracting relevant data. The included studies were evaluated against a standardized set of quality criteria. Results Our search yielded 3411 records. We included 48 studies, of which 37 were conducted in the USA and Canada, with 22 focusing Hispanic and Asian immigrants’ functional health literacy; the nine European studies examined a variety of work migrants and refugees using a comprehensive approach (e.g. measured by the HLS-EU-Q47). Thus, a strong heterogeneity in defining and measuring health literacy and in the populations examined can be stated. 15 studies exclusively examined the health literacy of women; none dealt with men only. Conclusions The heterogeneity in defining and measuring health literacy in migrants as well as the diversity of the populations studied make it difficult to compare international research in this area. There is a lack of research focusing male migrants. Key messages International research on health literacy with gender-specific data on migrants reveals a strong heterogeneity in defining and measuring health literacy. International research on health literacy with gender-specific data on migrants reveals a lack of studies regarding male migrants’ health literacy.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".