Patient-reported access to primary care in Ontario
Notice bibliographique
Résumé
Objective To describe patient-reported access to primary health care across 4 organizational models of primary care in Ontario, and to explore how access is associated with patient, provider, and practice characteristics. Design Cross-sectional survey. Setting One hundred thirty-seven randomly selected primary care practices in Ontario using 1 of 4 delivery models (fee for service, established capitation, reformed capitation, and community health centres). Participants Patients included were at least 18 years of age, were not severely ill or cognitively impaired, were not known to the survey administrator, had consenting providers at 1 of the participating primary care practices, and were able to communicate in English or French either directly or through a translator. Main outcome measures Patient-reported access was measured by a 4-item scale derived from the previously validated adult version of the Primary Care Assessment Tool. Questions were asked about physician availability during and outside of regular office hours and access to health information via telephone. Responses to the scale were normalized, with higher scores reflecting greater patient-reported access. Linear regressions were used to identify characteristics independently associated with access to care. Results Established capitation model practices had the highest patient-reported access, although the difference in scores between models was small. Our multilevel regression model identified several patient factors that were significantly ( P = .05) associated with higher patient-reported access, including older age, female sex, good-to-excellent self-reported health, less mental health disability, and not working. Provider experience (measured as years since graduation) was the only provider or practice characteristic independently associated with improved patient-reported access. Conclusion This study adds to what is known about access to primary care. The study found that established capitation models outperformed all the other organizational models, including reformed capitation models, independent of provider and practice variables save provider experience. This suggests that the capitation models might provide better access to care and that it might take time to realize the benefits of organizational reforms.
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 | 0,000 | 0,000 |
| 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 | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».