Examining critical factors affecting graduate retention from an emergency medicine training program in Addis Ababa, Ethiopia: a qualitative study of stakeholder perspectives
Bibliographic record
Abstract
BACKGROUND: In Ethiopia, improvement and innovation of the emergency care system is hindered by lack of specialist doctors trained in emergency medicine, underdeveloped emergency care infrastructure, and resource limitations. Our aim was to examine the critical factors affecting retention of graduates from the Addis Ababa University (AAU) post-graduate emergency medicine (EM) training program within the Ethiopian health care system. METHODS: One post-graduate trainee and one program manager from the AAU and the University of Toronto (UT) partnership conducted qualitative interviews with current AAU EM residents and stakeholders in Ethiopian EM. Qualitative inductive thematic analysis was performed. RESULTS: Within each domain, priority themes emerged from the responses, including the importance of career satisfaction over the career continuum (individual condition), the opportunity to be involved in the developing EM program and challenges associated with resource, economic, and employment constraints (occupational environment), and perceptions regarding the state of awareness of EM and the capacity for change at the societal level (national context). CONCLUSION: This work underscores the need to continue to address multiple systemic and cultural issues within the Ethiopian health care landscape in order to address EM graduate retention. It also highlights the potential success of a retention strategy focused on the career ambitions of keen EM doctors.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".