The Hidden Curricula of Medical Education: A Scoping Review
Bibliographic record
Abstract
PURPOSE: To analyze the plural definitions and applications of the term "hidden curriculum" within the medical education literature and to propose a conceptual framework for conducting future research on the topic. METHOD: The authors conducted a literature search of nine online databases, seeking articles published on the hidden, informal, or implicit curriculum in medical education prior to March 2017. Two reviewers independently screened articles with set inclusion criteria and performed kappa coefficient tests to evaluate interreviewer reliability. They extracted, coded, and analyzed key data, using grounded theory methodology. RESULTS: The authors uncovered 3,747 articles relating to the hidden curriculum in medical education. Of these, they selected 197 articles for full review. Use of the term "hidden curriculum" has expanded substantially since 2012. U.S. and Canadian medical schools are the focus of two-thirds of the empirical hidden curriculum studies; data from African and South American schools are nearly absent. Few quantitative techniques to measure the hidden curriculum exist. The "hidden curriculum" is understood as a mostly negative concept. Its definition varies widely, but can be understood via four conceptual boundaries: (1) institutional-organizational, (2) interpersonal-social, (3) contextual-cultural, and/or (4) motivational-psychological. CONCLUSIONS: Future medical education researchers should make clear the conceptual boundary or boundaries they are applying to the term "hidden curriculum," move away from general musings on its effects, and focus on specific methods for improving the powerful hidden curriculum.
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 | 0.026 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.034 | 0.029 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".