Critical Realism and Realist Inquiry in Medical Education
Bibliographic record
Abstract
Understanding complex interventions, such as in medical education, requires a philosophy of science that can explain how and why things work, or fail to work, in different contexts. Critical realism and its operationalization in the form of realist inquiry provides this explanatory power. Ontologically, critical realism posits that the social world is real, that it exists independent of our knowledge of it, and that it is driven by causal mechanisms. However, unlike postpositivism, a realist epistemological position is that our understanding of the mechanisms that underlay social reality is limited and subjective. Critical realism is focused on understanding the mechanisms that drive social reality even when they are not directly observable. One of the most commonly used methodologies in the critical realist paradigm is realist inquiry, which focuses on the relationships between context, mechanisms, and outcomes. At its core, realist inquiry is concerned with "What works for whom, under what circumstances, how, and why?" To that end, realist inquiry explores the mechanisms that drive social systems and the ways in which these mechanisms work to develop explanatory theories of the phenomena under consideration. Although, compared with other approaches, realist inquiry is relatively new in medical education, the value of realist inquiry is in its ability to model how complex interventions function differently across multiple contexts, explaining what works, how it works, for whom, and in what contexts.
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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 | 0.033 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.089 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".