Evaluating a research training programme for frontline health workers in conflict-affected and fragile settings in the middle east
Bibliographic record
Abstract
BACKGROUND: Health Research Capacity Building (HRCB) is key to improving research production among health workers in LMICs to inform related policies and reduce health disparities in conflict settings. However, few HRCB programmes are available in the MENA region, and few evaluations of HRCB globally are reported in the literature. METHODS: Through a qualitative longitudinal design, we evaluated the first implementation of the Center for Research and Education in the Ecology of War (CREEW) fellowship. Semi-structured interviews were conducted with fellows (n = 5) throughout the programme at key phases during their completion of courses and at each research phase. Additional data was collected from supervisors and peers of fellows at their organizations. Data were analysed using qualitative content analysis and presented under pre-identified themes. RESULTS: Despite the success of most fellows in learning on how to conduct research on AMR in conflict settings and completing the fellowship by producing research outputs, important challenges were identified. Results are categorized under predefined categories of (1) course delivery, (2) proposal development, (3) IRB application, (4) data collection, (5) data analysis, (6) manuscript write-up, (7) long-term effects, and (8) mentorship and networking. CONCLUSION: The CREEW model, based on this evaluation, shows potential to be replicable and scalable to other contexts and other health-related topics. Detailed discussion and analysis are presented in the manuscript and synthesized recommendations are highlighted for future programmes to consider during the design, implementation, and evaluation of such programmes.
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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 | 0.089 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".