The Hidden Curriculum: A Good Thing?
Bibliographic record
Abstract
Introduction The hidden curriculum is defined as a set of influences that function at the level of the organizational structure and culture to impact learning. Literature supports the significant impact of the hidden curriculum on all levels of learners in medical education. Our project aims to capture the messages being delivered to healthcare providers at our local facility. Methods Multiple one-time educational sessions on the hidden curriculum were provided over a five-year period to healthcare professionals. Participants were asked to share personal examples of their lived experiences with the hidden curriculum. A thematic analysis of the responses was completed and coded by two independent reviewers. Results Participants consisted of medical students, residents, faculty physicians, and allied health professionals. Their experience of the hidden curriculum emerged in six main themes: Vulnerability, Hierarchy, Privilege, Navigation & Negotiation, Positivity, and Dehumanizing. Conclusion A minority of responses demonstrated the positive impact that the hidden curriculum can have on professional development. This project highlights the importance of formally addressing the hidden curriculum to capitalize on its impact on medical trainees. The results have inspired a project focusing on residents as the population of interest in their unique role as learners and preceptors.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".