Understanding Causation in Healthcare: An Introduction to Critical Realism
Bibliographic record
Abstract
Both healthcare providers and researchers in the health sciences are well rehearsed in asking the question 'What could be causing this'? and examining beyond the surface of observable symptoms or obvious factors to understand what is really occurring with patients and health services. Critical realism is a philosophical framework that can help in this inquiry as we attempt to make sense of the observable world. The aim of this article is to introduce critical realism and explore how it can help both healthcare providers and health science researchers to better understand causation through the mechanisms that generate events, despite those mechanisms often being unseen. The article reviews foundational concepts and examples framed in the healthcare setting to make the key principles, strengths and limitations of critical realism accessible for those who are just beginning their journey with this approach.
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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 | 0.032 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.071 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".