Integrating person-centred care and social justice: a model for practice with larger-bodied patients
Bibliographic record
Abstract
Person-centred care (PCC) has been touted as a promising paradigm for improving patients' experiences and outcomes, and the overall therapeutic environment for a range of health conditions, including obesity. While this approach represents an important shift away from a paternalistic and disease-focused paradigm, we argue that PCC must be explicitly informed by a social justice lens to achieve optimal conditions for health and well-being. We suggest that existing studies on PCC for obesity only go so far in achieving social justice goals as they operate within a biomedical model that by default pathologises excess weight and predetermines patients' goals as weight loss and/or management, regardless of patients' embodied experiences and desires. There remains a dearth of empirical research on what social justice-informed PCC looks like in practice with larger patients. This interview study fills a research gap by exploring the perspectives of 1) health practitioners (n=22) who take a critical, social justice-informed approach to weight and 2) larger patients (n=20) served by such practitioners. The research question that informed this paper was: What are the characteristics of social justice-informed PCC that play out in clinical interactions between healthcare practitioners and larger-bodied patients? We identified five themes, namely: 1) Integrating evidence-based practice with compassionate, narrative-based care; 2) Adopting a curious attitude about the patient's world; 3) Centring patients' own wisdom and expertise about their conditions; 4) Working within the constraints of the system to advocate for patients to receive equitable care; 5) Collaborating across professions and with community services to address the multifaceted nature of patient health. The findings illustrate that despite participants' diverse perspectives around weight and health, they shared a commitment to PCC by upholding patient self-determination and addressing weight stigma alongside other systemic factors that affect patient health outcomes.
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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 | 0.055 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.077 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.006 | 0.027 |
| Research integrity | 0.010 | 0.014 |
| 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".