Profiling patterns of patient experiences of access to care and continuity at team-based primary healthcare clinics
Notice bibliographique
Résumé
Context: Access to primary healthcare services is a core lever for reducing health inequalities. The ability to reach and engage in the care process varies considerably depending on patients’ socio-demographic characteristics which we need to understand to address inequitable access issues. Objective: To identify different profiles of access to care and continuity experiences of patients registered at team-based primary healthcare clinics. Study Design/Analysis: This cross-sectional study was conducted from September 2022 to April 2023. We used latent class analysis (LCA) to identify patients’ profiles based on nine components of access and continuity experiences and multinomial logistic regression to analyze their association with ten characteristics related to patient sociodemographic and their clinic characteristics. Setting: 104 PHC clinics across Quebec, Canada. Setting/Dataset: 121,570 registered patients over 18 years of age with an email address available in their electronic medical record. Measures: The optimal number of profiles (four) was determined using LCA measures (best model determined by AIC, BIC, and entropy). Results: "Easy access and continuity" (42%) was characterized by ease in almost all access and continuity components. Three profiles were characterized by diverging access and/or continuity difficulties: "Challenging booking" (32%) represented patients having to try several times to obtain an appointment at their clinic; "Challenging continuity" (9%) those having to repeat information that should have been in their file; "Access and continuity barriers" (16%) characterized difficulties with all access and continuity components. Female gender and poor perceived health significantly increased the risk of belonging to the three difficulties profiles by 1.5. Being a recently arrived immigrant (p=0.036), having less than a high school education (p=0.002) and being registered at a large clinic (p<0.001) were associated with experiencing booking difficulties. Having at least one chronic condition (p=0.004) or poor perceived mental health (p=0.048) were associated with experiencing continuity difficulties. Conclusions: Our results showed potential areas for improvement, such as facilitating appointment booking for recently arrived immigrants and patients with low education, integrating interprofessional collaboration practices for patients with chronic conditions and improving care coordination for patients with mental health needs.
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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 | 0,002 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 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 ».