Postgraduate Selection in Medical Education: A Scoping Review of Current Priorities and Values
Bibliographic record
Abstract
PURPOSE: The process of screening and selecting trainees for postgraduate training has evolved significantly in recent years, yet remains a daunting task. Postgraduate training directors seek ways to feasibly and defensibly select candidates, which has resulted in an explosion of literature seeking to identify root causes for the problems observed in postgraduate selection and generate viable solutions. The authors therefore conducted a scoping review to analyze the problems and priorities presented within the postgraduate selection literature to explore practical implications and present a research agenda. METHOD: Between May 2021 and February 2022, the authors searched PubMed, EMBASE, Web of Science, ERIC, and Google Scholar for English language literature published after 2000. Articles that described postgraduate selection were eligible for inclusion. 2,273 articles were ultimately eligible for inclusion. Thematic analysis was performed on a subset of 100 articles examining priorities and problems within postgraduate selection. Articles were sampled to ensure broad thematic and geographical variation across the breadth of articles that were eligible for inclusion. RESULTS: Five distinct perspectives or value statements were identified in the thematic analysis: (1) Using available metrics to predict performance in postgraduate training; (2) identifying the best applicants via competitive comparison; (3) seeking alignment between applicant and program in the selection process; (4) ensuring diversity, mitigation of bias, and equity in the selection process; and (5) optimizing the logistics or mechanics of the selection process. CONCLUSIONS: This review provides insight into the framing and value statements authors use to describe postgraduate selection within the literature. The identified value statements provide a window into the assumptions and subsequent implications of viewing postgraduate selection through each of these lenses. Future research must consider the outcomes and consequences of the value statement chosen and the impact on current and future approaches to postgraduate selection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".