Prevalence of multimorbidity in migrants, refugees, asylum seekers and displaced persons and association with mortality: A systematic review protocol
Bibliographic record
Abstract
Abstract Background Multimorbidity is the presence of two or more physical and/or mental long-term health conditions and is associated with aging, but also with socio-economic deprivation. Immigration status contributes to socio-economic deprivation, potentially increasing prevalence of multimorbidity. Prevalence of multimorbidity in different migrant populations, such as asylum seekers, refugees and displaced persons, and its effect on mortality is poorly understood and no systematic review on the topic exists to date. Objective To assess what is known about the prevalence of multimorbidity in refugees, asylum seekers, migrants and displaced persons and about the association, if any, between multimorbidity and mortality in these populations. Design Systematic review of the literature. The following electronic medical databases will be searched: MEDLINE, Embase, Scopus, CINAHL and The Cochrane Library. A narrative synthesis of findings will be undertaken, and meta-analysis considered if appropriate. Title, abstract and full paper screening will be undertaken independently by two reviewers. Studies will be limited to English and Spanish language and publication date from 2000 onward. The Newcastle-Ottawa Scale for Cohort Studies will be used as a quality assessment tool. This protocol adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA) 2020 guidelines. Conclusion Understanding the prevalence of multimorbidity amongst refugees, migrants, asylum seekers and displaced persons, and its association with mortality will provide valuable insights to inform practice and policy responses to increasing global migration.
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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 | 0.054 | 0.071 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.025 | 0.022 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.053 | 0.005 |
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".