Global Health Partnerships for Continuing Medical Education: Lessons from Successful Partnerships
Bibliographic record
Abstract
The past decade has witnessed an increase in global partnerships created to strengthen health systems and provide training to health professionals in low- and middle-income countries. These partnerships are complex interventions. This study focused on unpacking the characteristics of global partnerships that provide continuing education for health professionals. A realist approach underpinned the research design to identify the mechanisms that shape successful global partnerships. Two case studies focusing on global continuing medical education (CME) were studied longitudinally using a realist evaluation approach. To complement that finding, published research reports of global CME partnerships were synthesized using a realist synthesis approach. Data were collected over a three-year period and included interviews, participant observations, document reviews, and surveys. A hybrid thematic approach guided the data analysis. The study results suggested that global CME partnerships are highly dependent on human factors. On the one hand, motivational factors related to individual players help to shape the partnership goals, directions, and outcomes. On the other hand, relational factors such as trust, communication, and understanding play a key role in developing and sustaining global partnerships. As such, these partnerships highly rely on the individuals who champion the partnership at the country level or at the partnership level and in their ability to build relationships as well as empower key stakeholders.
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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 | 0.085 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.025 | 0.032 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".