An Axiological Analysis of One Medical School’s Admissions Process: Exploring Individual Values and Value Systems
Bibliographic record
Abstract
PURPOSE: Values and value systems are fundamental to medical school admissions processes. An axiological analysis was carried out to explore the individual values and value systems found within the University of Calgary's Cumming School of Medicine's undergraduate admissions process. METHOD: A mixed-methods case study methodology was developed with a focus on applicant characteristics viewed as desirable, the relative value ascribed to applicant characteristics, the values that participants in admissions processes brought to bear, the values that were reflected in the artifacts and procedures used in support of admissions processes, and the values that were expressed at a system, program, or institutional level. The study employed a descriptive audit of admissions processes, a stakeholder survey, stakeholder interviews, and a discourse analysis of admissions materials (all carried out between June and September 2017). RESULTS: The study found that, despite a general sense of satisfaction with the rigor of the admissions process, there was less satisfaction with the final selection it produced. Participants wanted to see more attention paid to responsibilities to patients and society than to gender and ethnic balance. CONCLUSIONS: Those involved with medical school admissions need to be mindful of their value systems and use them to align intent with process and outcomes in selecting tomorrow's physicians. Axiological analysis of medical education processes can play a central role in reviewing and refocusing efforts on meeting an institution's social mission and medical education's social contract.
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".