Weight stigma in mental healthcare: shifting towards a health-centric approach
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,051 | 0,078 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,003 | 0,008 |
| Communication savante | 0,008 | 0,011 |
| Science ouverte | 0,002 | 0,011 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».