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Primary Health Care Reform: Who joins a Family Medicine Group?

2012· dissertation· en· W7033322034 sur OpenAlexaboutno aff

Notice bibliographique

RevueeScholarship@McGill (McGill) · 2012
Typedissertation
Langueen
DomaineMedicine
ThématiqueAnorectal Disease Treatments and Outcomes
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHealth carePrimary carePopulationPrimary health careAmbulatory careService (business)Selection (genetic algorithm)Logistic regressionSet (abstract data type)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

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.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,018
score de la tête « metaresearch » (Gemma)0,043
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,280
Score d'incertitude au seuil0,556

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0180,043
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0020,003
Études des sciences et des technologies0,0080,005
Communication savante0,0080,005
Science ouverte0,0020,004
Intégrité de la recherche0,0040,003
Charge utile insuffisante (le modèle a refusé de juger)0,0100,001

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.

Tête enseignante Opus0,023
Tête enseignante GPT0,293
Écart entre enseignants0,270 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2012
Routes d'admission1
Résumé présentoui

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