Role modelling to support careers in general practice: a realist review protocol
Bibliographic record
Abstract
BACKGROUND: Role models encountered during undergraduate training play an important part in shaping future doctors. They can act as powerful attractants towards, and deterrents away from, a career in general practice. Many GP educators, who act as role models, are burnt-out and wish to leave the profession, which may limit their ability to influence students positively, with consequent detrimental impact on recruitment to the specialty. AIM: A realist review will be undertaken, aiming to explore how, why, and for whom role modelling in undergraduate medical education can support medical students towards careers in general practice. DESIGN & SETTING: The realist review will follow Pawson's five steps, including: locating existing theories; searching for evidence; article selection; data extraction; and synthesising evidence and drawing conclusions. It will explore literature published in the English language between 2013 and 2024. METHOD: An initial explanatory framework (initial programme theory; IPT) will be developed, guided by a stakeholder panel including medical undergraduates, GPs, and patient and public representatives. Searches will be developed and conducted in electronic databases and grey literature. Studies will be included if they explore the relationship between GP role modelling and undergraduate career choice, and relevant data will be extracted. CONCLUSION: Findings will refine the IPT, unveiling key contexts, mechanisms, and outcomes that influence role modelling in undergraduate GP medical education and support or deter students from careers in general practice. These findings will support recommendations and interventions to facilitate positive outcomes, including improved recruitment to general practice.
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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 | 0.142 | 0.142 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.071 | 0.015 |
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".