Addressing the complexity of equitable care for larger patients: A critical realist framework
Bibliographic record
Abstract
The notion of obesity as a pathological state within the individual remains the dominant perspective in public health and biomedicine. However, there has been a growing call to re-examine this assumption from a social justice lens. Given that obesity is itself a contested term, there is a lack of consensus on what constitutes a social justice approach to addressing weight and health. Underpinning such debates amongst social justice researchers is a divide between realist and constructionist framings of obesity. The realist framing considers obesity to be a biomedical fact posing health and social consequences, and proposes collective, systems-based solutions to preventing and managing obesity. In contrast, the constructionist framing challenges the taken-for-granted assumptions that obesity is an epidemic and that fat necessarily signifies poor health. Despite such theorizing about social justice and obesity, to-date no empirical research has explored the views and experiences of 1) social justice-oriented healthcare practitioners who work with larger patients or 2) larger patients who receive social justice-informed care. This article features interviews with practitioners (n = 22) across multiple professions in Canada who describe themselves as adopting a social justice approach to caring for larger patients, as well as with practitioners' patients (n = 20) who self-identify as larger bodied. Drawing on a critical realist theoretical framework, the analysis uncovers the ontological and ideological assumptions driving participants' varying conceptualizations of obesity. To conclude, the article considers how participants’ different understandings of weight and health impact clinical practice and interactions, particularly in terms of whether social justice goals are fulfilled.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.030 | 0.231 |
| Scholarly communication | 0.032 | 0.027 |
| Open science | 0.008 | 0.023 |
| Research integrity | 0.015 | 0.026 |
| Insufficient payload (model declined to judge) | 0.003 | 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".