Primary Health Care Reform: Who joins a Family Medicine Group?
Notice bibliographique
Résumé
Reorganization of primary health care is being actively pursued and new models of primary health care delivery are being developed in the U.S. and in several Canadian provinces. In Quebec, Family Medicine Groups (FMGs) were created in 2002 in order to provide enhanced access and better coordination of care through a team based approach to primary care. Previous research on new models of primary health care has often failed to evaluate their effects within a causal inference framework, and little attention has been paid to the type of physicians and patients that voluntarily join them. Understanding who is attracted to new models is not only important to adjust for selection bias, but it may affect future reforms by helping to elucidate what would happen if FMGs were implemented on a population level. This thesis attempts to understand the voluntary selection of patients and physicians into Family Medicine Groups in Quebec, Canada. A longitudinal administrative dataset of vulnerable patients (elderly or chronically ill) from the Régie de l'assurance maladie du Québec (RAMQ) has been divided between FMG and non-FMG users, and includes information on demographic characteristics, chronic illnesses and ambulatory and tertiary health service use before the advent of FMGs. Physicians of these patients are characterized by their FMG status, demographics, and practice and patient characteristics before FMGs are in place. Multivariate regression is used to identify key predictors of joining a FMG among both patients and physicians. Lastly, comparable physician and patient populations are created using propensity scores in order to set up the evaluation of health outcomes, utilization of services and costs in the years after joining a FMG. The distribution of propensity scores and their ability to balance key covariates after different matching and weighting techniques was investigated. Results of the analysis reveal that geographic location, socio-economic status, visits in an ambulatory setting, emergency room visits, hospitalizations and having a usual provider of care are all factors which affect the probability of a patient joining a FMG. Specifically, residents of remote regions, low socio-economic status and those who use emergency rooms and hospitals more often are more likely to be enrolled, whereas patients that use ambulatory services and have a usual provider of care are less likely to be enrolled. Similarly, it is shown that factors that affect a physician's likelihood of joining a FMG include time since graduation, geographic region and revenue from traditional fee-for-service vs. other sources. Younger physicians and those who practice in a local community centre (CLSC) and short term/acute inpatient hospital care (CHSCD) are more likely to participate. Propensity scores were able to balance the pre-treatment differences, and this finding is robust across different mechanisms of adjusting for the propensity score. Overall, it was shown that participation in a FMG is not a random process and any further research on the effect of FMGs, or any other type of primary health care reform, should consider this. Accounting for the type of patients that join different models, by using propensity score analysis for example, will be critical to forming evidence based policy recommendations. Particular consideration for geographic location, patients' morbidity, socio-economic status, health service use, as well as physicians' age and experience working in other settings is needed.
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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 | 0,018 | 0,043 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,008 | 0,005 |
| Communication savante | 0,008 | 0,005 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,001 |
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 ».