Bridging the gap: Improving CASPer test confidence and competency for underrepresented minorities in medicine through interactive peer-assisted learning
Bibliographic record
Abstract
BACKGROUND: The Computer-based Assessment for Sampling Personal Characteristics (CASPer) is a situational judgement test (SJT) adopted by medical schools to assess applicants' interpersonal skills. CASPer applicants must compose their responses to ethical dilemmas, thereby highlighting the applicant's rationale for ethical decision-making. Minority applicants usually lack access to a network of individuals who can offer guidance and expertise on ethical decision-making. As such, this study investigated the impact of a CASPer coaching program designed for minority applicants. METHODS: A free online intervention was designed to help minority applicants prepare for the CASPer test. The program consisted of 35 learners and three medical student tutors. Important attributes of the 4-week program included free access to a medical ethics book, feedback provision to in-class and homework student responses, and facilitation of a mock CASPer. Course feedback was collected. Additionally, a pre and post-program survey was administered to assess learners' competence and confidence surrounding CASPer test-taking. RESULTS: < 0.05). CONCLUSIONS: Through peer-to-peer teaching and access to medical student mentors, our program addresses socioeconomic barriers that several minority applicants face when applying to medical school.
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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 | 0.001 | 0.240 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.015 | 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".