Strengthening effective preventive services for refugee populations: toward communities of solution
Bibliographic record
Abstract
Refugee populations have unequal access to primary care and may not receive appropriate health screening or preventive service recommendations. They encounter numerous health care disadvantages as a consequence of low-income status, race and ethnicity, lower educational achievement, varying degrees of health literacy, and limited English proficiency. Refugees may not initially embrace the concept of preventive care, as these services may have been unavailable in their countries of origin, or may not be congruent with their beliefs on health care. Effective interventions in primary care include the appropriate use of culturally and linguistically trained interpreters for health care visits and use of evidence-based guidelines. Effective approaches for the delivery of preventive health and wellness services require community engagement and collaborations between public health and primary care. In order to provide optimal preventive and longitudinal screening services for refugees, policies and practice should be guided by unimpeded access to robust primary care systems. These systems should implement evidence-based guidelines, comprehensive health coverage, and evaluation of process and preventive care outcomes.
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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 | 0.043 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.005 | 0.038 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 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".