Decentralised training for medical students: a scoping review
Bibliographic record
Abstract
BACKGROUND: Increasingly, medical students are trained at sites away from the tertiary academic health centre. A growing body of literature identifies the benefits of decentralised clinical training for students, the health services and the community. A scoping review was done to identify approaches to decentralised training, how these have been implemented and what the outcomes of these approaches have been in an effort to provide a knowledge base towards developing a model for decentralised training for undergraduate medical students in lower and middle-income countries (LMICs). METHODS: Using a comprehensive search strategy, the following databases were searched, namely EBSCO Host, ERIC, HRH Global Resources, Index Medicus, MEDLINE and WHO Repository, generating 3383 references. The review team identified 288 key additional records from other sources. Using prespecified eligibility criteria, the publications were screened through several rounds. Variables for the data-charting process were developed, and the data were entered into a custom-made online Smartsheet database. The data were analysed qualitatively and quantitatively. RESULTS: One hundred and five articles were included. Terminology most commonly used to describe decentralised training included 'rural', 'community based' and 'longitudinal rural'. The publications largely originated from Australia, the United States of America (USA), Canada and South Africa. Fifty-five percent described decentralised training rotations for periods of more than six months. Thematic analysis of the literature on practice in decentralised medical training identified four themes, each with a number of subthemes. These themes were student learning, the training environment, the role of the community, and leadership and governance. CONCLUSIONS: Evident from our findings are the multiplicity and interconnectedness of factors that characterise approaches to decentralised training. The student experience is nested within a particular context that is framed by the leadership and governance that direct it, and the site and the community in which the training is happening. Each decentralised site is seen to have its own dynamic that may foreground certain elements, responding differently to enabling student learning and influencing the student experience. The insights that have been established through this review have relevance in informing the further expansion of decentralised clinical training, including in LMIC contexts.
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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 | 0.026 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.015 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".