Practice guidelines on migrants’ health: assessment of their quality and reporting
Bibliographic record
Abstract
BACKGROUND: Migrants may carry with them communicable and non communicable diseases as they move to the host country. Screening migrants may help in improving their health status and in preventing the spread of infections to the host population. OBJECTIVE: To identify and assess the quality of published practice guidelines addressing migrants' health. METHODS: We included practice guidelines addressing migrants' health at the clinical, public health or health systems levels. We searched Medline, Embase, the National Guideline Clearinghouse and the Canadian Medical Association's Clinical Practice Guidelines Database. Two teams of two reviewers conducted in duplicate and independent manner study selection, data abstraction, assessment of the guideline quality (using the AGREE II instrument), and assessment of the quality of the reporting (using the RIGHT statement). RESULTS: Out of 2732 citations captured by the electronic search, we included 24 eligible practice guidelines, all addressing the level of post-arrival to the host country and published between 2011 and 2017. The majority of guidelines (57%) addressed non-communicable diseases, 95% addressed screening, while 52% addressed prevention and treatment respectively. The majority of the guidelines reported their funding sources. 86% used the GRADE approach as part of the development process. The included guidelines scored high on the majority of the items, and low on the following two domains of the AGREE II instrument: rigor of development and applicability. The mean number of the RIGHT checklist items met by the included guidelines was 27, out of a total of 35. Most of the guidelines were based on systematic reviews (95.6%). A minority of the included guidelines (26%) reported considering the values and preferences of the target populations or the costs and resource implications (30%) in the formulation of recommendations. CONCLUSION: We identified 23 practice guidelines addressing migrants' health, the majority of which addressed screening services. The vast majority of the captured guidelines targeted screening because the population of interest is migrants, meaning that the intention of the guidelines is to deal with additional factors than usual ones, such as prevalence of disease in country of origin, endemic diseases and others. The guidelines suffered limitations on two quality domains (rigor of development and applicability), and have room for improvement of their reporting.
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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 | 0.320 | 0.695 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.029 | 0.026 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".