An examination of Eyal & Hurst’s (2008) framework for promoting retention in resource-poor settings through locally-relevant training: A case study for the University of Guyana Surgical Training Program
Bibliographic record
Abstract
BACKGROUND: Eyal and Hurst proposed that locally relevant medical education can offset the prevalence of physician "brain drain" in resource-poor regions of the world, and presented a framework of the ethical and pragmatic benefits and concerns posed by these initiatives. The present study explored the framework's utility through a case study of the University of Guyana Diploma in Surgery (UGDS) program. METHODS: The framework's utility was evaluated using a case study design that included review and analysis of documents and semi-structured interviews with graduates, trainees, faculty members, and policy makers associated with the UGDS program. Data were analyzed from constructivist and interpretivist perspectives, and compared against the benefits and concerns described by Eyal and Hurst. RESULTS: The framework is a useful template for capturing the breadth of experience of locally relevant training in the Guyanese setting. However, the results suggest that delineating the framework factors as either beneficial or concerning may constrict its applicability. The case study design also provided specific insights about the UGDS program, which indicate that the Program has promoted the retention of graduates and a sustainable culture of postgraduate medical education in Guyana. CONCLUSION: It is suggested that the framework be modified so as to represent the benefits and concerns of locally relevant training along a continuum of advantage. These approaches may help us understand retention within a resource-poor country, but also within particularly remote areas and public health care systems generally.
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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 | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.021 | 0.017 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| 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".