APPLIED RESEARCH: Reflecting the Relative Values of Community, Faculty, and Students in the Admissions Tools of Medical School
Bibliographic record
Abstract
BACKGROUND: In defining the characteristics of medical students that society and the medical profession find desirable, little effort has been spent assessing the relative value of the dozens of characteristics that have been identified. Furthermore, many institutions go to great lengths to ensure equal representation across stakeholder groups in an effort to maximize the heterogeneity of the pool of students accepted to study medicine; however, the extent to which different stakeholders value different characteristics has yet to be determined. PURPOSE: This study was an attempt to assess the relative value of the characteristics of medical students that society and the medical profession find desirable. METHODS: Using documents created internationally to identify the core competencies of medical personnel, a series of 7 characteristics were generated for inclusion in a study that adopted the paired comparison technique. Of 347 surveyed, 292 respondents indicated the rank ordering they would assign to each characteristic by circling the more important characteristic in all possible pairings. RESULTS: Overwhelmingly, "ethical" was deemed to be the most important characteristic on which selection tools should be based. Surprisingly, the pattern of responses was highly consistent regardless of stakeholder group and degree of affiliation with the undergraduate medical program. CONCLUSIONS: The generalizable features of this study not only include the empirical findings but also demonstrate useful survey protocol that can be adapted by any admission committee to guide the generation of an institution-specific admissions blueprint. A novel protocol that provides the necessary flexibility is discussed.
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 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 | 0.057 | 0.165 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".