A Logic Model Framework for Planning an International Refugee Health Research, Evaluation, and Ethics Committee
Bibliographic record
Abstract
Collaborative approaches to supporting the health of refugees and other newcomer populations in their resettlement country are needed to address the complex medical and social challenges they may experience after arrival. Refugee health professionals within the Society of Refugee Healthcare Providers (SRHP)-the largest medical society dedicated to refugee health in North America-have expressed interest in greater research collaborations across SRHP membership and a need for guidance in conducting ethical research on refugee health. This article describes a logic model framework for planning the SRHP Research, Evaluation, and Ethics Committee. A logic model was developed to outline the priorities, inputs, outputs, outcomes, assumptions, external factors, and evaluation plan for the committee. The short-term outcomes include (1) establish professional standards in refugee health research, (2) support evaluation of existing refugee health structures and programs, and (3) establish and disseminate an ethical framework for refugee health research. The SRHP Research, Evaluation, and Ethics Committee found the logic model to be an effective planning tool. The model presented here could support the planning of other research committees aimed at helping to achieve health equity for resettled refugee populations.
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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 | 0.114 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| 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".