The CASPER preparation program innovation: increasing self-perceived competence and confidence of underrepresented applicants on the novel CASPER Snapshot and CanMEDS roles
Bibliographic record
Abstract
BACKGROUND: Underrepresented Minorities in Medicine (URMMs) may face financial and social limitations when matriculating into medical schools. Performance on situational judgment tests such as Computer-based Assessment for Sampling Personal Characteristics (CASPER) can be enhanced by coaching and mentorship. The CASPER Preparation Program (CPP) coaches URMMs to prepare for the CASPER test. During the coronavirus 2019 pandemic (COVID-19), CPP implemented novel curricula on the CASPER Snapshot and CanMEDS roles. METHODS: Pre and post-program questionnaires were completed by the students, which assessed their: 1) confidence in understanding the CanMEDS roles, and 2) perceived confidence in performing well and their familiarity and preparedness with the CASPER Snapshot. With a second post-program questionnaire, participants' scores on the CASPER test as well as medical school application outcome were also assessed. RESULTS: Participants reported a significant increase in the URMMs' knowledge, self-perceived competency to complete the CASPER Snapshot, and their anxiety significantly decreased. The level of confidence in understanding CanMEDS roles for a career in healthcare increased as well. The majority (91%) agreed that the feedback received from tutors was adequate and the virtual component of the program was beneficial during COVID-19. 51% of students scored in the highest quartile on the CASPER test and 35% received an offer of admission from CASPER-requiring medical schools. CONCLUSION: Pathway coaching programs have the potential to increase confidence and familiarity amongst URMMs for the CASPER tests and CanMEDS roles. Similar programs should be developed with the aim to increase the chances of URMMs matriculating into medical schools.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".