Using critical consciousness to inform health professions education: A literature review
Bibliographic record
Abstract
PURPOSE: To explore how, in health professions education (HPE), the concept of critical consciousness has been defined and discussed, and to consider and suggest how critical pedagogy could be applied in practice. This exploration responds to increasing calls in the literature for HPE to foster compassionate care and social consciousness through the social sciences and humanities. METHOD: The authors searched Medline/PubMed, ERIC and Web of Science for articles focusing on critical consciousness and/or critical pedagogy involving health professions. A thematic analysis aimed to identify key themes of critical consciousness in HPE literature. RESULTS: The authors included 30 papers in their review. Key themes related to defining and discussing core attributes of critical consciousness in HPE were: 1) appreciating context in education and practice; 2) illuminating power structures; 3) moving beyond 'procedural'; 4) enacting reflection; and 5) promoting equity and social justice. CONCLUSIONS: Critical consciousness may inform an appropriate critical pedagogy for fostering compassionate, humanistic, socially conscious health professionals who act as agents of change. While the authors share critical teaching practices for educators, considerable care must be taken in efforts to use critical pedagogy within the current structures of HPE programmes. The authors suggest attending to the philosophical and theoretical origins of critical consciousness and those of the dominant models of contemporary HPE (e. g. competency-based approaches) in order to ensure the tenets of critical pedagogy can be enacted authentically.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.023 | 0.025 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".