Examining the effect of a Mini Med School using social cognitive career theory
Bibliographic record
Abstract
Background: Diversity of professionals within the healthcare system enhances patient outcomes. Existing literature indicates that Mini Medical School (MMS) Programs can increase medical school diversity by engaging youth from underrepresented backgrounds (URiM+); however, there is limited understanding of the mechanisms by which this happens. Further research could refine these programs and improve their effectiveness. Grounded in social cognitive career theory, this study evaluates the impact of a single-day MMS on URiM+ students' knowledge and confidence in pursuing medicine. Methods: Female and gender-diverse youth were invited to urban or rural single-day MMS events organized by medical students. These MMS programs included clinical skills activities and a lecture about becoming a physician. Participants completed a pre- and post-event survey and quiz assessing their interest, knowledge, and confidence in pursuing a medical career. Results: Participants at both MMS events reported increased confidence about pursuing a career in medicine. Both subjective and objective measures of knowledge about a career in medicine increased. Interest in pursuing a career in medicine, however, did not increase significantly in either group. A significant positive correlation was found between participants' self-confidence in becoming a physician and their perceived knowledge of how to become a physician. Conclusions: We found that these single-day MMS programs increased participants' knowledge about the steps to pursuing a career in medicine, and their confidence in their ability to do so, but did not significantly increase their interest. When considering the impacts of MMS programs from a social cognitive career theory lens, program organizers should consider ensuring that their MMS curriculum includes practical tools for success, as this will contribute to supporting URiM+ students in viewing themselves as future physicians and contributing to the aim of diversifying the medical profession.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".