Understanding patient preferences on providing sociodemographic information in an acute care setting: a qualitative study
Notice bibliographique
Résumé
BACKGROUND: Access to patient sociodemographic information is a critical factor to understanding, at a systems-level, where health inequities exist so they can be addressed. Patient concerns around disclosing personal information remain a barrier to sociodemographic data collection. Our objective was to assess patient perceptions regarding the routine collection of sociodemographic information in an acute care hospital setting. METHODS: We conducted a qualitative study using the Framework Method to understand patient perceptions regarding sociodemographic data collection. We administered semi-structured interviews with patients admitted to the General Internal Medicine and Geriatric Medicine units at a university-affiliated hospital in Toronto, Canada. Two reviewers independently coded 10% of interview transcripts until a kappa ≥ 0.7 was achieved. The remaining interviews were single-coded. Data were analyzed using thematic analysis. RESULTS: A total of 52 qualitative interviews were conducted between December 2021 and September 2023. Of the 52 individuals interviewed, 21 also agreed to complete a sociodemographic survey (40%). Among these participants, 57% (n = 12) were women and 62% (n = 13) were White. Patients felt more comfortable disclosing sociodemographic data if they believed it would lead to better or more equitable care; data were collected after they were admitted to hospital; data were collected verbally; and concerns about data privacy, anonymization, use, and secure storage were addressed. Some patients expressed discomfort being asked questions about income or race. Most patients had no preference regarding who from the healthcare team collected the sociodemographic data. Participants reported the importance of a friendly and respectful approach of the person collecting the data. CONCLUSIONS: Participants reported feeling comfortable disclosing their sociodemographic information in hospital; however, only 40% were willing to complete a demographic questionnaire. Participants’ comfort levels were impacted by the approach of the individual asking the questions, the types of questions asked, when the data were collected, and whether assurances around privacy and transparency regarding the use of data were provided. The results of this study should be used to develop strategies to support the implementation of routine sociodemographic data collection in acute care settings.
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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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 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) | 0,000 | 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 ».