The dawn of family medicine in Ethiopia.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: This article describes the development of the first training program in family medicine in Ethiopia that was launched on February 4, 2013, at Addis Ababa University (AAU). The postgraduate program will prepare highly trained doctors for all parts of the country who choose generalism for their lifelong career. The paper describes a series of strategies that were used from 2008 to 2013 to take the Ethiopian family medicine program from vision to reality. There is no single model for the development of family medicine in a country where it does not yet exist. In this case the strategies included Continuing Medical Education events, discussions with stakeholders, international collaboration, needs assessment, curriculum design, and faculty development. The article also reviews both the potential for a new program in family medicine to contribute to the country's health system plus the challenges that are expected in the early stages of establishing a new specialty. The challenges include the ambiguous roles of the family physician in the Ethiopian health care system, uncertainty about career opportunities, adaptation of the curriculum to address local needs, expansion of the training programs to produce larger numbers of family physicians, development of Ethiopian faculty who will be teachers of family medicine, and internal and external brain drain. Family physicians will need to maintain a respectful relationship with other specialist physicians as well as nonphysician primary care providers. The development of this AAU family medicine residency is an example of a successful inter- institutional relationship between local and international partners to create a sustainable, Ethiopian-led training program. Insights from this article may guide development of similar training programs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".