Constructing critical thinking in health professional education
Bibliographic record
Abstract
INTRODUCTION: Calls for enabling 'critical thinking' are ubiquitous in health professional education. However, there is little agreement in the literature or in practice as to what this term means and efforts to generate a universal definition have found limited traction. Moreover, the variability observed might suggest that multiplicity has value that the quest for universal definitions has failed to capture. In this study, we sought to map the multiple conceptions of critical thinking in circulation in health professional education to understand the relationships and tensions between them. METHODS: We used an inductive, qualitative approach to explore conceptions of critical thinking with educators from four health professions: medicine, nursing, pharmacy, and social work. Four participants from each profession participated in two individual in-depth semi-structured interviews, the latter of which induced reflection on a visual depiction of results generated from the first set of interviews. RESULTS: Three main conceptions of critical thinking were identified: biomedical, humanist, and social justice-oriented critical thinking. 'Biomedical critical thinking' was the dominant conception. While each conception had distinct features, the particular conceptions of critical thinking espoused by individual participants were not stable within or between interviews. DISCUSSION: Multiple conceptions of critical thinking likely offer educators the ability to express diverse beliefs about what 'good thinking' means in variable contexts. The findings suggest that any single definition of critical thinking in the health professions will be inherently contentious and, we argue, should be. Such debates, when made visible to educators and trainees, can be highly productive.
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 | 0.048 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.009 | 0.073 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".