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Enregistrement W4220677030 · doi:10.1186/s40337-022-00570-5

Challenges in eating disorder diagnosis and management among family physicians and trainees: a qualitative study

2022· article· en· W4220677030 sur OpenAlex

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Notice bibliographique

RevueJournal of Eating Disorders · 2022
Typearticle
Langueen
DomainePsychology
ThématiqueEating Disorders and Behaviors
Établissements canadiensHamilton Health SciencesMcMaster UniversityImpactUniversity of British Columbia
Organismes subventionnairesMcMaster University
Mots-clésFocus groupFamily medicineThematic analysisMedicineInterimQualitative researchNonprobability samplingDistressNursingClinical psychologyPopulation

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Family physicians are one of the first points of contact for individuals with eating disorders (EDs) seeking care and treatment, but training in this area is suboptimal and insufficient. Specialized ED treatment programs often have long wait lists, and family physicians are responsible for patients care in the interim. The aim of this study was to identify the learning needs and challenges faced by Canadian family physicians and trainees when caring for patients with EDs. METHODS: We recruited six family medicine residents and five family physicians practicing in an academic unit in the Department of Family Medicine of a medical school in urban southwestern Ontario, Canada. We used purposive sampling, focusing on residents and faculty physicians from the department and conducted one focus group for the residents and another for the faculty physicians, exploring their clinical knowledge and challenges when managing ED patients. The focus groups were audio-recorded and transcribed verbatim prior to thematic coding. RESULTS: Physicians and residents faced challenges in discussing, screening, and managing patients with EDs. Three themes that emerged from the qualitative data highlighted training needs related to: (a) improving communication skills when treating a patient with an ED, (b) more effective screening and diagnosis in primary care practice, and (c) optimizing management strategies for patients with an ED, especially patients who are waiting for more intensive treatment. A fourth theme that emerged was the distress experienced by family physicians as they try best to manage and access care for their patients with EDs. CONCLUSION: Addressing the learning needs identified in this study through continuing education offerings could aid family physicians in confidently providing effective, evidence-based care to patients with EDs. Improvement in training and education could also alleviate some of the distress faced by family physicians in managing patients with EDs. Ultimately, system changes to allow more efficient and appropriate levels of care for patients with EDs, removing the burden from family medicine, are critical as EDs are on the rise. A person with an eating disorder will normally seek care from their family physician first. These conditions can dramatically reduce the quality of a person's life and health. Family physicians therefore need to know how best to help these patients or refer them to a more intensive level of care, which often has long wait lists. We asked a group of family physicians and a group of family medicine trainees about their experiences with patients with eating disorders and about the information they wished they had to help these patients. The results show that they need more information on how to talk to a patient about eating disorders without judgement, how to diagnose a patient with an eating disorder, and then what treatment and management is needed while they wait for more intensive treatment for sicker patients. The physicians and trainees both talked about the stress and worry that they faced when treating patients with eating disorders. Besides their lack of training about these conditions, family physicians also described difficulties when trying to access timely specialized services for their patients. Physicians can experience moral distress when they know that their patients need higher level care, but there are systemic barriers to specialized programs that block their patients from getting the care they need when they need it.

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.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,237
Score d'incertitude au seuil0,932

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,055
Tête enseignante GPT0,366
Écart entre enseignants0,311 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle