Medical residents reflect on their prejudices toward poverty: a photovoice training project
Notice bibliographique
Résumé
BACKGROUND: Clinicians face challenges in delivering care to socioeconomically disadvantaged patients. While both the public and academic sectors recognize the importance of addressing social inequities in healthcare, there is room for improvement in the training of family physicians, who report being ill-equipped to provide care that is responsive to the living conditions of these patients. This study explored: (i) residents' perceptions and experience in relation to providing care for socioeconomically disadvantaged patients, and (ii) how participating in a photovoice study helped them uncover and examine some of their prejudices and assumptions about poverty. METHODS: We conducted a participatory photovoice study. Participants were four family medicine residents, two medical supervisors, and two researchers. Residents attended six photovoice meetings at which they discussed photos they had taken. In collaboration with the researchers, the participants defined the research questions, took photos, and participated in data analysis and results dissemination. Meetings were recorded and transcribed for analysis, which consisted of coding, peer debriefing, thematic analysis, and interpretation. RESULTS: The medical residents uncovered and examined their own prejudices and misconceptions about poverty. They reported feeling unprepared to provide care to socioeconomically disadvantaged patients. Supported by medical supervisors and researchers, the residents underwent a three-phase reflexive process of: (1) engaging reflexively, (2) break(ing) through, and (3) taking action. The results indicated that medical residents subsequently felt encouraged to adopt a care approach that helped them overcome the social distance between themselves and their socioeconomically disadvantaged patients. CONCLUSIONS: This study highlights the importance of providing medical training on issues related to poverty and increasing awareness about social inequalities in medical education to counteract prejudices toward socioeconomically disadvantaged patients. Future studies should examine which elective courses and training could provide suitable tools to clinicians to improve their competence in delivering care to socioeconomically disadvantaged patients.
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 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 | 0,004 | 0,044 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».