Leveraging the Added Value of Experiential Co-Curricular Programs to Humanize Medical Education
Bibliographic record
Abstract
Background: The aftermath of the 1910 Flexner report resulted in significant gaps in the structure of medical education. Experiential co-curricular opportunities can contribute to addressing these gaps. Purpose: To explore, from a holistic social constructionism perspective, the added value of a co-curricular program, designed and implemented based on Kolb’s Experiential Learning Theory. Methodology/Approach: In this case study, randomly selected medical students, who had participated in an experiential co-curricular program, undertook focus group sessions. Data were inductively analyzed using thematic analysis based on constructivist epistemology. Findings/Conclusions: Benefits at the individual/student level included three interlinked themes: personal, academic, and professional development. The personal development theme related to building character and resilience, and the academic development theme related to application of theory and previously acquired knowledge. Four categories surfaced within the professional development theme. Emergent categories at the community level were institutional advancement, contribution to host centers, and giving back to the community. Implications: Co-curricular programs, that are based on Kolb’s Experiential Learning Theory (ELT) and that foster learning as participation in the social world, humanize medical education, and nurture holistic millennial physicians.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".