Evidence-based nursing practices in primary care services for migrants: a systematic review
Bibliographic record
Abstract
Experiencing migration differently affects the health of individuals according to additional factors of vulnerability as gender or legal status. In primary care services, nurses are key actors in caring for such vulnerable migrants but evidence-based practices are still lacking. This literature review aims at identifying evidence-based nursing practices in primary care services for vulnerable migrant populations. Methods. We identified nursing interventions caring for migrants in primary care services in the literature published between 2000 and 2015. Vulnerability of the migrants was measured with the PROGRESS framework. Nursing components of the interventions were analyzed according to the 6 dimensions of the model of Advance Practice Nursing (Hamric et al. 2014). Quality appraisal included 8 criteria, each study received a score of quality. Results. We reviewed 104 papers and found 30 studies that met the inclusion criteria. Being vulnerable as a migrant was mostly defined as not being able to communicate with the health professionals (n = 20). Most interventions targeted type 2 diabetes (n = 8) and cardiovascular diseases (n = 8). Attention to the specific needs of the migrants was achieved through cultural and linguistic adaptations but only one intervention included the intersectionality of risk factors (e.g. paying attention to migration, gender and socioeconomic status). Expert guidance/coaching and consultation were the two advanced practice nursing components reported in all studies. Collaboration, leadership, research and ethical decision-making skills were not reported in the retrieved studies. Overall, nursing interventions appear to be effective in improving health outcomes for migrants. Conclusion This literature review provides a relevant basis for researches and professional development for nurses. Further studies should support the development of collaboration, leadership, research and ethical decision-making skills of nurses in primary care services. Key messages Nursing interventions appear to be effective in improving health outcomes for migrants Further studies should support the development of collaboration, leadership, research and ethical decision-making skills of nurses in primary care services
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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 | 0.012 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".