Transforming community-based primary health care delivery through comprehensive performance measurement and reporting: examining the influence of context
Notice bibliographique
Résumé
BACKGROUND: Community-based primary health care represents various community-based health care (CBPHC) models that incorporate health promotion and community development to deliver first-contact health services. Learning health systems (LHSs) are essential for improving CBPHC in which feedback from relevant stakeholders is used to continuously improve health systems with the goal of achieving population health and health equity. Performance reporting is one way to present data to clinicians and decision makers to facilitate a process of reflection, participation, and collaboration among partners to improve CBPHC. METHODS: Our objective was to obtain feedback on a regional CBPHC performance portrait through key informant interviews. We used purposive convenience sampling to recruit participants who were clinicians in primary care and/or decision-makers in primary care at a regional level. The performance portrait summarized results of survey questions asked of patients, providers, and primary care organizations. The portrait was organized by the 10 pillars of the Patient's Medical Home (PMH) model. Interview questions specifically asked about portrait content, formatting, interpretability, utility, and dissemination strategies. Content analysis was used to analyze interview data. RESULTS: We completed 19 interviews with key informants from the Canadian provinces of Nova Scotia (n = 8), Ontario (n = 6) and British Columbia (n = 5). We coded transcripts into four content areas: (1) Usability as influenced by content and interpretability, (2) Formatting, (3) Utility, and (4) Dissemination. Using data and reporting back to clinicians and decision-makers about how their practices and jurisdictions are performing in primary care in meaningful ways is important. Our results suggest having available methodology notes, including the analysis used to develop any scoring, sampling and sample sizes, and interpretation of the statistics is necessary. CONCLUSIONS: This research was the first to create a comprehensive performance portrait using data driven by factors that are important to primary care partners. We obtained important feedback on the portrait in the context of usability, formatting, utility, and dissemination. This data needs to be used to provide feedback in continuous cycles to evaluate and improve CBPHC models as part of a LHS.
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| 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 ».