Evidence-based medicine, shared decision making and the hidden curriculum: a qualitative content analysis
Bibliographic record
Abstract
INTRODUCTION: Medical education should portray evidence-based medicine (EBM) and shared decision making (SDM) as central to patient care. However, misconceptions regarding EBM and SDM are common in clinical practice, and these biases might unintentionally be transmitted to medical trainees through a hidden curriculum. The current study explores how assumptions of EBM and SDM can be hidden in formal curriculum material such as PowerPoint slides. METHODS: We conducted a qualitative content analysis using a purposive sample of 18 PowerPoints on the management of upper respiratory tract infections. We identified concepts pertaining to decision making using theory-driven codes taken from the fields of EBM and SDM. We then re-analyzed the coded text using a constructivist latent thematic approach to develop a rich description of conceptualizations of decision making in relation to EBM and SDM frameworks. RESULTS: PowerPoint slides can relay a hidden curriculum, which can normalize: pathophysiological reasoning, unexplained variations in clinical care, the use of EBM mimics, defensive medicine, an unrealistic portrayal of benefits, and paternalism. DISCUSSION: Addressing the hidden curriculum in formal curricular material should be explored as a novel strategy to foster a positive attitude towards EBM and SDM and to improve patient outcomes by encouraging the use of these skills.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".