Core Personal Competencies Important to Entering Students’ Success in Medical School
Bibliographic record
Abstract
Assessing applicants' personal competencies in the admission process has proven difficult because there is not an agreed-on set of personal competencies for entering medical students. In addition, there are questions about the measurement properties and costs of currently available assessment tools. The Association of American Medical College's Innovation Lab Working Group (ILWG) and Admissions Initiative therefore engaged in a multistep, multiyear process to identify personal competencies important to entering students' success in medical school as well as ways to measure them early in the admission process. To identify core personal competencies, they conducted literature reviews, surveyed U.S and Canadian medical school admission officers, and solicited input from the admission community. To identify tools with the potential to provide data in time for pre-interview screening, they reviewed the higher education and employment literature and evaluated tools' psychometric properties, group differences, risk of coaching/faking, likely applicant and admission officer reactions, costs, and scalability. This process resulted in a list of nine core personal competencies rated by stakeholders as very or extremely important for entering medical students: ethical responsibility to self and others; reliability and dependability; service orientation; social skills; capacity for improvement; resilience and adaptability; cultural competence; oral communication; and teamwork. The ILWG's research suggests that some tools hold promise for assessing personal competencies, but the authors caution that none are perfect for all situations. They recommend that multiple tools be used to evaluate information about applicants' personal competencies in deciding whom to interview.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".