Raising awareness of anti-fat stigma in healthcare through lived experience education: a continuing professional development pilot study
Notice bibliographique
Résumé
BACKGROUND: Anti-fat attitudes and weight-based discrimination are prevalent in healthcare settings and among healthcare practitioners and clinical trainees, and can result in immense harm to patients. There is increasing recognition that anti-fat bias in healthcare is a critical issue that must be addressed, but there is a dearth of evidence demonstrating sustained attitude and behavioural change among clinicians, illustrating a need for more innovative educational approaches and rigorous evaluation. We describe the co-design and delivery of a narrative-based continuing professional development curriculum aimed at raising awareness of weight-based bias and stigma. METHODS: Our research team of lived experience educators, clinicians and researchers collaboratively developed a series of seven podcast episodes comprised of narrative descriptions of lived experiences with and impacts of weight bias, stigma and discrimination in healthcare settings, as well as a post-podcast workshop to facilitate reflection and discussion between participants. The curriculum was piloted among 20 clinicians practicing at a large urban hospital in Mississauga, Canada. We explored feasibility, acceptability and learning impact by analyzing responses to questionnaires completed following each podcast episode and responses shared during the workshops and follow-up feedback sessions. RESULTS: We observed high acceptability and feasibility of the curriculum. Participants experienced the podcast as a practical and convenient learning format and the workshop as a valuable opportunity to collectively debrief and reflect. The learning impact of the curriculum was strong; participants described a range of emotions elicited by the podcasts, engaged in self-reflection, and expressed a desire to modify clinical approaches. Barriers to the application of learnings identified by participants include pervasiveness of the use of body mass index (BMI) as an indicator of risk and a criterion for referral; discomfort with difficult conversations; prevalent biomedical understandings about the association between weight and health; and clinicians' defensiveness. CONCLUSION: This pilot study yielded promising findings and demonstrated potential impact on weight bias and stigma among healthcare providers. Necessary next steps include conducting larger scale, rigorous evaluations of the curriculum among broader populations, both health professions trainees and current healthcare providers.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 tête enseignante, 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 ».