A Logic Model Framework for Planning an International Refugee Health Research, Evaluation, and Ethics Committee
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it