Critical Thinking in Health Sciences Education: Considering “Three Waves”
Bibliographic record
Abstract
Historically, health science education has focused on content knowledge. However, there has been increasing recognition that education must focus more on the thinking processes required of future health professionals. In an effort to teach these processes, educators of health science students have looked to the concept of critical thinking. But what does it mean to “think critically”? Despite some attempts to clarify and define critical thinking in health science education and in other fields, it remains a “complex and controversial notion that is difficult to define and, consequently, difficult to study” (Abrami et al., 2008, p. 1103). This selected review offers a roadmap of the various understandings of critical thinking currently in circulation. We will survey three prevalent traditions from which critical thinking theory emerges and the major features of the discourses associated with them: critical thinking as a set of technical skills, as a humanistic mode of accessing creativity and exploring self, and as a mode of ideology critique with a goal of emancipation. The goal of this literature review is to explore the various ways in which critical thinking is understood in the literature, how and from where those understandings emerge, and the debates that shape each understanding.
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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 | 0.031 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.007 | 0.044 |
| Scholarly communication | 0.021 | 0.033 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".