Assessing innovative approaches for global health capacity building in fragile settings in the MENA region: development of the evaluation of capacity building (eCAP) program
Bibliographic record
Abstract
BACKGROUND: Given the magnitude and frequency of conflicts in the MENA region along with their devastating impact on health responses and outcomes, there exists a strong need to invest in contextualized, innovative, and accessible capacity building approaches to enhance leadership and skills in global health. The MENA region suffers from limited (1) continued educational and career progression opportunities, (2) gender balance, and (3) skill-mix among its health workforce, which require significant attention. MAIN TEXT: The Global Health Institute at the American University of Beirut incepted the Academy division to develop and implement various global health capacity building (GHCB) initiatives to address those challenges in fragile settings across low-and middle-income countries in the MENA region. These initiatives play a strategic role in this context, especially given their focus on being accessible through employing innovative learning modalities. However, there exists a dearth of evidence-based knowledge on best practices and recommendations to optimize the design, implementation, and evaluation of GHCB in fragile settings in the MENA region. The present paper describes the development of the evaluation of capacity building program (eCAP), implemented under the Academy division, to assess the effectiveness of its initiatives. eCAP is composed of 3 phases: (1) a situational assessment, followed by (2) production of multiple case studies, and finally (3) a meta-assessment leading to model development. The goal of eCAP is not only to inform the Academy's operations, but also to synthesize produced knowledge into the formation of an evidence-based, scalable, and replicable model for GHCB in fragile settings. CONCLUSION: eCAP is an important initiative for researchers, educators, and practitioners interested in GHCB in fragile settings. Several lessons can be learned from the outcomes it has yielded so far in its first two phases of implementation, ranging from the situational assessment to the production of evaluation case studies, which are expanded on in the manuscript along with pertinent challenges.
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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 | 0.217 | 0.188 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".