Facilitators and barriers of sociodemographic data collection in Canadian health care settings: a multisite case study evaluation
Notice bibliographique
Résumé
BACKGROUND: Despite growing awareness of the importance of social determinants of health, research remains limited about the implementation of sociodemographic data collection in Canadian health care settings. Little is known about the salient contextual factors that enable or hinder collection and use of social information to improve quality of care in clinical settings. This study examines the perceptions and experiences of managers and care providers to better understand how to support organizational efforts to collect and use sociodemographic data to provide equity-oriented care. METHODS: Case studies of three diverse urban health care settings employed semi-structured individual and group interviews with managers and care providers respectively to explore their experiences with implementation. Data was analyzed separately and in context for each site as part of an individual case study. A thematic analysis of interview transcripts was performed with an inductive approach to coding of segments of the text. Constructs of the Consolidated Framework for Implementation Research (CFIR) were used as an analytical framework to structure the data to support cross case comparisons of facilitators and barriers to implementation across settings. RESULTS: Several perceived facilitators and barriers to implementation were identified that clustered around three CFIR domains: intervention, inner setting and characteristics of individuals. Macro level (outer setting) factors were relatively unexplored. Sites were motivated by their recognition of need for social information to improve quality of care. Organizational readiness for implementation was demonstrated by priorities that reflected concern for equity in care, leadership support and commitment to an inclusive process for stakeholder engagement. Barriers included perceived relevance of only a subset of sociodemographic questions to service delivery, staff capacity and comfort with data collection as well as adequate resources (funding and time). CONCLUSION: Although system level mandates were underexplored, they may accelerate adoption and implementation of sociodemographic data collection in the presence of organizational readiness. Standardized tools integrated into information systems and workflows would support adequately trained personnel. More research is needed to understand important factors in rural health settings and with clinical application to inform care delivery pathways.
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 | 0,026 | 0,005 |
| 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 | 0,000 |
| 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 ».