Evaluation of global health capacity building initiatives in low-and middle-income countries: A systematic review
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
BACKGROUND: Low- and middle-income countries (LMICs) are in dire need to improve their health outcomes. Although Global Health Capacity Building (GHCB) initiatives are recommended approaches, they risk being ineffective in the absence of standardized evaluation methods. This study systematically reviews evaluation approaches for GHCB initiatives in LMICs. METHODS: We searched the Medline (OVID), PubMed, Scopus, and Embase.com databases for studies reporting evaluation of a GHCB initiative in a LMIC from January 1, 2009 until August 15, 2019. To differentiate them from intervention, prevention, and awareness initiatives, included articles reported at least one approach to evaluate their learning modality. We excluded cross-sectional studies, reviews, and book chapters that only assessed the effect of interventions. Data identifying the learning modality, and evaluation method, level, time interval, and approach were extracted from articles as primary outcomes. RESULTS: Of 8324 identified studies, 63 articles were eligible for analysis. Most studies stemmed from Africa and Asia (69.8%), were delivered and evaluated face-to-face (74.6% and 76.2%), mainly to professionals (57.1%) and community workers (20.6%). Although the use of online and blended modalities showed an increase over the past 4 years, only face-to-face initiatives were evaluated long-term beyond individual-level. GHCB evaluations in general lacked standardization especially regarding the tools. CONCLUSION: This is an important resource for evaluating GHCB initiatives in LMICs. It synthesizes evaluation approaches, offers recommendations for improvement, and calls for the standardization of evaluations, especially for long-term and wider impact assessment of online and blended modalities.
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 | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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