Towards universal health care coverage in low- and middle- income countries: integrating refugees into national health systems
Bibliographic record
Abstract
Globally, the number of people forcibly displaced has reached an unprecedented record surpassing 100 million, including 26.3 million refugees, in May 2022 [ 1 ]. The vast majority of the world’s refugees (83%) are hosted by and living outside of camps amongst national populations in low- and middle-income countries (LMICs) [ 2 ]; often adjacent to the countries experiencing crises or conflict. Many of these host countries are themselves already experiencing political instability and limited resources. This, in turn, hinders their ability to provide adequate healthcare access - a fundamental human right - to refugees and nationals alike. The situation is further complicated by the protracted nature of current refugee crises and the tremendous health needs of refugee populations encompassing communicable and non-communicable diseases, notably mental health conditions, and injuries including those linked to gender-based violence (GBV).
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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 | 0.023 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 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".