Humanism, the Hidden Curriculum, and Educational Reform
Bibliographic record
Abstract
BACKGROUND: Medical educators have used the hidden curriculum concept for over three decades to make visible the effects of tacit learning, including how culture, structures, and institutions influence professional identity formation. In response to calls to see more humanistic-oriented training in medicine, the authors examined how the hidden curriculum construct has been applied in the English language medical education literature with a particular (and centering) look at its use within literature pertaining to humanism. They also explored the ends to which the hidden curriculum construct has been used in educational reform efforts (at the individual, organizational, and/or systems levels) related to nurturing and/or increasing humanism in health care. METHOD: The authors conducted a scoping review and thematic analysis that draws from the tradition of critical discourse analysis. They identified 1,887 texts in the literature search, of which 200 met inclusion criteria. RESULTS: The analysis documents a strong preoccupation with negative effects of the hidden curriculum, particularly the moral erosion of physicians and the perceived undermining of humanistic values in health care. A conflation between professionalism and humanism was noted. Proposals for reform largely target medical students and medical school faculty, with very little consideration for how organizations, institutions, and sociopolitical relations more broadly contribute to problematic behaviors. CONCLUSIONS: The authors argue that there is a need to transcend conceptualizations of the hidden curriculum as antithetical to humanism and offer suggestions for future research that explores the necessity and value of humanism and the hidden curriculum in medical education and training.
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 | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.053 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".