The Weighty Burden of Inequity Experienced by Patients in Larger Bodies: Fostering Equitable Treatment in the Naturopathic Community
Bibliographic record
Abstract
Individuals identified as overweight or obese (people in larger bodies) often endure poor health equity as a result of pervasive stigmatization and discrimination due to their weight, in both social and healthcare settings. Often referred to as 'weight bias', people in larger bodies are differentially, and inequitably, treated specifically due to their weight. This inequitable treatment results in deleterious health effects, such as poorer mental health, increased risk of mortality, avoidance to seek care, social isolation, and disadvantageous physiologic changes (e.g. elevated C-reactive protein). In an effort to foster equitable, inclusive, and fair treatment of all patient groups accessing naturopathic care, this critical reflection and narrative literature review was undertaken in order to explore important considerations specifically for people in larger bodies. Further, it may serve as a guide for naturopathic doctors (NDs) to appreciate the sensitivity of terminology, the complexity of weight-related research, the caution that must be taken with social media use and the unintentional, but likely, harms of hyperfocusing on weight. A call for actionable changes is relayed in order to provide the ND community with tangible and achievable goals to consciously work towards in order to foster equitable care and treatment of all patients, regardless of body size.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".