Weight stigma in mental healthcare: shifting towards a health-centric approach
Bibliographic record
Abstract
Systemic weight-bias may negatively influence nutrition recommendations and outcomes in the treatment of mental illness(1,2,3). However, weight loss is often considered a primary outcome in mental health care, despite the potential harm that may come from practising within a ‘weight-centric’ paradigm(4). Therefore, it is important to consider the impact of experiences of weight-based discrimination in mental health care, as well as investigate weight-neutral approaches in relation to mental and physical health and wellbeing. This study utilised a sequential explanatory study design. First, a systematic search was performed including observational studies of adult populations, with ≥ 1 mental or physical health outcome, and ≥ 1 validated measure of eating behaviour reflective of a weight-neutral approach. Outcomes were categorised into four domains (mental health, physical health, health promoting behaviours and other eating behaviours). Risk of bias was assessed using the Newcastle-Ottawa Scale. Next, a cross-sectional online survey was conducted among a community sample with self-reported diagnoses of depression or anxiety. Questions collected experiences of weight-stigma in mental health care, and validated measures such as the Depression and Anxiety Stress Scale (DASS-21), Stigmatizing Situations Inventory-Brief (SSI-B), and Weight Bias Internalization Scale (WBIS-M). Quantitative data were statistically analysed using Jamovi, while open-ended responses were thematically analysed using an inductive approach to reach consensus. In the systematic search, 8281 records were identified with 86 studies including 75 unique datasets, and 78 unique exposures including intuitive eating (n = 48), mindful eating (n = 19), and eating competence (n = 11). Eating behaviours were significantly related to lower levels of disordered eating, and depressive symptoms, and greater body image, self-compassion, diet quality, and higher fruit and vegetable intake. Among the 66 survey respondents (mean age 35.5 ± 11y), greater experienced weight bias (SSI-B) was significantly associated with greater depressive symptoms (r = 0.281, p < 0.05), and greater internalised weight-bias (WBIS-M) was significantly associated with greater depressive symptoms (r = 0.492, p < 0.001; β = 0.414, p = 0.001), anxiety symptoms (r = 0.437, p < 0.001; β = 0.390, p = 0.003), stress (r = 0.399, p < 0.01; β = 0.371, p = 0.006) and DASS-21 total score (r = 0.513, p < 0.001; β = 0.453, p < 0.001). Respondents reported experiences of weight-stigma that resulted in the mismanagement of mental health concerns, unsolicited diet and weight loss advice, and healthcare avoidance. Experiences of weight-stigma within mental health care have the potential to negatively impact mental health and nutrition-related recommendations. However, it must be considered that eating behaviours focused on health, not weight, are positively related to a range of mental and physical health outcomes. Therefore, it is vital healthcare professionals understand and assess their own biases related to weight, to reduce the impact of weight-bias on quality of care and consider weight-neutral approaches to better support mental health and wellbeing.
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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 | 0.051 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| 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".