Pre-clerkship Exploration of Underrepresented Specialties: Participant Perceptions
Bibliographic record
Abstract
Background: Background: Exposure to specialties significantly influences medical student career decisions; however, many students feel they are not adequately introduced to particular specialties until the end of their undergraduate training, if at all. Therefore, the Pre-clerkship Residency Exploration Program (PREP) was established. PREP was designed to reduce concerns regarding career decisions, while increasing exposure to specialties that traditionally receive less exposure in medical school curricula. Methods: PREP was a two-week elective available to second year medical students (n = 40) comprising five components: clinical electives, panel discussions, procedural skills circuits, simulations, and specialty-specific workshops. Participants rotated through ten electives and engaged in panel discussions focused on career choices and decisions. Skills circuits and simulations introduced students to procedures and scenarios they could encounter during PREP elective rotations. Specialty-specific workshops were held by several departments to build interest and introduce students to under-represented specialties. Results: PREP was assessed using the Kirkpatrick model, a framework that evaluates the effectiveness of training. PREP significantly increased students’ comfort with making career decisions, while reducing concerns related to a lack of exposure to various specialties (p < 0.0001) and time constraints with determining career options (p < 0.0001). Furthermore, PREP directly impacted career aspirations with 80.6% of participants changing their top-three career choices after completing the program. PREP is a valuable addition to medical school education and offers a novel approach to supporting students’ informed career decisions as well as increase their exposure to specialties which are underrepresented in medical school curricula. Discussion: We are currently in discussion with several Canadian medical schools about implementing PREP at their universities. Future research will analyze if participation in PREP translates to increased application rates to underrepresented specialties. To accomplish this objective, we will follow cohorts of PREP participants through the residency matching process and compare outcomes with historical data.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".