Opening Editorial: Selection and Recruitment in Medical Education
Bibliographic record
Abstract
There is over a century of research on selection and recruitment and the field has both developed and expanded significantly over this time. Previous research has tended to focus on reviewing the effectiveness of selection methods (academic records, references, personal statements, aptitude tests, personality assessments, situational judgement tests, and interviews), where good quality evidence is now emerging. Many challenges remain however, reflecting that selection and recruitment into medical education (both undergraduate and postgraduate) is a complex, multi-dimensional, dynamic phenomenon. For example, issues regarding diversity and fairness in selection have been researched over many years but there remains a huge gap between the research evidence and policy enactment in many parts of the globe. In this opening editorial for our special issue on selection and recruitment in medical education we encourage authors to consider six key question areas (amongst others), including: how will technology (e.g. social media, big data, artificial intelligence, etc) influence selection research and practices in future? should selection criteria be reviewed to include creativity, innovation, resilience and adaptability (beyond heavy reliance on prior academic attainment as the main criterion)? is selection for medical education fair? How do we address issues regarding widening participation and diversity in practice? to what extent do political, cultural and social factors influence selection philosophy and policies internationally? what are the risks to effective selection (e.g. access to coaching, legal challenge of poor practices) and, a new Ottawa consensus on selection and recruitment has been published - to what extent does this statement reflect your experiences of designing and implementing selection systems in your locality? In contributing to the debate, this special issue provides a platform for authors to present the latest research, empirical studies, systematic reviews, reflections, case studies and practical tips on current/future issues in selection and recruitment in medical education.
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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 | 0.119 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.022 | 0.026 |
| Insufficient payload (model declined to judge) | 0.029 | 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".