Core Personal Competencies Important to Entering Students’ Success in Medical School
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.095 | 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 it