Family Medicine in Ethiopia: Lessons from a Global Collaboration
Bibliographic record
Abstract
BACKGROUND: Building the capacity of local health systems to provide high-quality, self-sustaining medical education and health care is the central purpose for many global health partnerships (GHPs). Since 2001, our global partner consortium collaborated to establish Family Medicine in Ethiopia; the first Ethiopian family physicians graduated in February 2016. METHODS: The authors, representing the primary Ethiopian, Canadian, and American partners in the GHP, identified obstacles, accomplishments, opportunities, errors, and observations from the years preceding residency launch and the first 3 years of the residency. RESULTS: Common themes were identified through personal reflection and presented as lessons to guide future GHPs. LESSON 1: Promote Family Medicine as a distinct specialty. LESSON 2: Avoid gaps, conflict, and redundancy in partner priorities and activities. LESSON 3: Building relationships takes time and shared experiences. LESSON 4: Communicate frequently to create opportunities for success. LESSON 5: Engage local leaders to build sustainable, long-lasting programs from the beginning of the partnership. CONCLUSIONS: GHPs can benefit individual participants, their organizations, and their communities served. Engaging with numerous partners may also result in challenges-conflicting expectations, misinterpretations, and duplication or gaps in efforts. The lessons discussed in this article may be used to inform GHP planning and interactions to maximize benefits and minimize mishaps.
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.
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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 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".