Advancing the health literacy of migrants in second-language courses: Realist review
Bibliographic record
Abstract
Background: Migrants and refugees are high-risk populations with often limited health literacy (HL). It is crucial to improve their HL early after arrival. One promising approach is to combine language and HL learning in second-language courses. Objectives: This study reviewed empirical evidence on the promotion of migrants’ HL in second-language courses and developed a theory of change to inform innovative projects. Methodology: We conducted a systematic realist review of HL in second language courses in seven scientific databases and a grey literature search. After screening titles, abstracts, and full texts, we identified 21 eligible publications from 13 programs. We systematically analyzed program context and characteristics, evaluation design, and outcomes and developed a theory of change based on the findings. Results: Programs promoting HL in second language courses are diverse in terms of contextual factors, formats, study designs, measures of HL, language improvements, effectiveness, relevant outcomes, and enabling factors. All studies reported improved HL after the program, but to varying degrees. The findings regarding second language improvement are mixed. Seven core components of HL as a social practice emerged. Numerous factors influencing course implementation, outcomes, and sustainability were described in detail. We conceptualized a theory of change for the HL promotion in second language courses. Conclusion: This realist review presents ample empirical evidence that second language courses can promote HL in various ways. However, these courses are complex, heterogeneous, and dependent on multiple factors. Despite these limitations, second language courses show promise as innovative and effective approaches that reach many migrants. Therefore, health promoters, medical professionals, adult educators, and policymakers should collaborate nationally and internationally to leverage this unique opportunity. The theory of change can serve as a blueprint for discussing existing programs thoroughly and developing promising, effective, and sustainable programs.
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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 | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".