Measuring equity in per capita primary care investment in Ontario: Challenges for data linkage and analysis
Notice bibliographique
Résumé
IntroductionFifteen years ago almost all primary care physicians (PCPs) were paid fee-for-service. Now, many physicians receive other payments as well, including capitation payments, incentives and bonuses and funding for other health professionals. It is challenging to track these changes in primary care payment and understand how they relate to individual patients. Objectives and ApproachThe objectives of this study were to assess changes in PCP payments from 2002/03 to 2011/12 and examine differences in per capita investment by urban-rural status, recent arrival (proxy for immigrant status) and income quintile. This required a three-step approach: assigning payments to physicians, assigning patients to physicians and then apportioning the payments by patient. Payments were apportioned based on the type of payment and how the data were captured. For example, capitation payments were paid monthly, but without any detail as to which patients they were for, so all capitation payments were summed and apportioned among all rostered patients. ResultsAll PCPs for whom we had payment data and to whom patients could be assigned were included. Three types of physician-patient 'relationships' were identified: the patient was on the physician's formal roster; the patient was 'virtually' rostered to the physician who provided the plurality of their care; or the patient was part of the physician's overall panel, which includes all patients seen during the year, rostered and not. The type of relationship determined which payment were allocated to each patient. When the $3.5B in payments were apportioned and different populations compared, we found inequities in new primary care investment by income, immigrant status and rurality. For example, we found a disproportionate investment in interdisciplinary teams for non-immigrant Ontarians living in more well-off suburban areas. Conclusion/ImplicationsEstimating per capita primary care investment is a challenging but worthwhile undertaking. The results of this study suggest that the Government of Ontario should facilitate increased participation in new primary care models by immigrants and people living in major urban centres.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,073 | 0,160 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,006 | 0,028 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,007 | 0,002 |
| Science ouverte | 0,004 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 source (Gemma direct ou Codex distillé), 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 ».