Assessing evidence of interventions addressing inequity among migrant populations: a two-stage systematic review
Bibliographic record
Abstract
BACKGROUND: Everyone has the right to achieve the standard of health and well-being. Migrants are considered as vulnerable populations due to the lack of access to health services and financial protection in health. Several interventions have been developed to improve migrant population health, but little is known about whether these interventions have considered the issue of equity as part of their outcome measurement. OBJECTIVE: To assess the evidence of health interventions in addressing inequity among migrants. METHODS: We adopted a two-stage searching approach to ensure the feasibility of this review. First, reviews of interventions for migrants were searched from five databases: PubMed, Cochrane, CINAHL, PsycINFO, and EMBASE until June 2017. Second, full articles included in the identified reviews were retrieved. Primary studies included in the identified reviews were then evaluated as to whether they met the following criteria: experimental studies which include equity aspects as part of their outcome measurement, based on equity attributes defined by PROGRESS-Plus factors (place of residence, race/ethnicity, occupation, gender, religion, education, socio-economic status, social capital, and others). We analysed the information extracted from the selected articles based on the PRISMA-Equity guidelines and the PROGRESS-Plus factors. RESULTS: Forty-nine reviews involving 1145 primary studies met the first-stage inclusion criteria. After exclusion of 764 studies, the remaining 381 experimental studies were assessed. Thirteen out of 381 experimental studies (3.41%) were found to include equity attributes as part of their outcome measurement. However, although some associations were found none of the included studies demonstrated the effect of the intervention on reducing inequity. All studies were conducted in high-income countries. The interventions included individual directed, community education and peer navigator-related interventions. CONCLUSIONS: Current evidence reveals that there is a paucity of studies assessing equity attributes of health interventions developed for migrant populations. This indicates that equity has not been receiving attention in these studies of migrant populations. More attention to equity-focused outcome assessment is needed to help policy-makers to consider all relevant outcomes for sound decision making concerning migrants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.077 | 0.169 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.022 | 0.020 |
| Bibliometrics | 0.030 | 0.021 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".