Toward ‘seeing’ critically: a Bayesian analysis of the impacts of a critical pedagogy
Bibliographic record
Abstract
Critical reflection supports enactment of the social roles of care, like collaboration and advocacy. We require evidence that links critical teaching approaches to future critically reflective practice. We thus asked: does a theory-informed approach to teaching critical reflection influence what learners talk about (i.e. topics of discussion) and how they talk (i.e. whether they talk in critically reflective ways) during subsequent learning experiences? Pre-clinical students (n = 75) were randomized into control and intervention conditions (8 groups each, of up to 5 interprofessional students). Participants completed an online Social Determinants of Health (SDoH) module, followed by either: a SDoH discussion (control) or critically reflective dialogue (intervention). Participants then experienced a common learning session (homecare curriculum and debrief) as outcome assessment, and another similar session one-week later. Blinded coders coded transcripts for what (topics) was said and how (critically reflective or not). We constructed Bayesian regression models for the probability of meaning units (unique utterances) being coded as particular what codes and as critically reflective or not (how). Groups exposed to the intervention were more likely, in a subsequent learning experience, to talk in a critically reflective manner (how) (0.096 [0.04, 0.15]) about similar content (no meaningful differences in what was said). This difference waned at one-week follow up. We showed experimentally that a particular critical pedagogical approach can make learners' subsequent talk, ways of seeing, more critically reflective even when talking about similar topics. This study offers the field important new options for studying historically challenging-to-evaluate impacts and supports theoretical assertions about the potential of critical pedagogies.
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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 | 0.098 | 0.374 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".