Interventions to improve immigrant health. A scoping review
Bibliographic record
Abstract
Background: : Disparities in health between immigrants and their host populations have been described across countries and continents. Hence, interventions for improving health targeting general populations are not necessarily effective for immigrants. To conduct a systematic search of the literature evaluating health interventions for immigrants; to map the characteristics of identified studies including range of interventions, immigrant populations and their host countries, clinical areas targeted and reported evaluations, challenges and limitations of the interventions identified. Following the results, to develop recommendations for research in the field. A scoping review approach was chosen to provide an overview of the type, extent and quantity of research available. Studies were included if they empirically evaluated health interventions targeting immigrants and/or their descendants, included a control group, and were published in English (PubMed and Embase from 1990 to 2015). Most of the 83 studies included were conducted in the USA, encompassed few immigrant groups and used a randomized controlled trial (RCT) or cluster RCT design. Most interventions addressed chronic and non-communicable diseases and attendance at cancer screening services, used individual targeted approaches, targeted adult women and recruited participants from health centres. Outcome measures were often subjective, with the exception of interventions for cardiovascular risk and diabetes. Generally, authors claimed that interventions were beneficial, despite a number of reported limitations. Recommendations for enhancing interventions to improve immigrant health are provided to help researchers, funders and health care commissioners when deciding upon the scope, nature and design of future research in this area.
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 | 0.025 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".