Access granted! barriers endure: determinants of difficulties accessing specialist care when required in Ontario, Canada
Notice bibliographique
Résumé
BACKGROUND: In the Canadian context, health care services are governed by the Canada Health Act, which ensures that primary care doctors, specialists, hospitals and dental surgeries are covered through provincial health insurance plans. This ensures access to medically necessary health care services for all Canadians regardless of ability to pay. Despite this important piece of legislation, research has shown persistent inequalities in access between and within socio-demographic groups, and geographic areas. To date, most research has focused on access to primary care, with much less attention paid to specialist care as an important component of the health care continuum. Thus, the objectives of this research are to address this gap in knowledge by examining the factors associated with difficulty accessing specialist services, and the reasons why particular subpopulation groups report experiencing difficulties. METHODS: This research uses multivariate logistic regression to analyze data from the Canadian Community Health Surveys' optional content from the province of Ontario (n=21,526) related to accessing specialist health care services. The multivariate logistic regression model identifies several subpopulation groups that are more likely to report difficulty accessing specialist care when required. Cross-tabulations are subsequently used to establish the main reasons why difficulties are faced. RESULTS: Over 26% of respondents required a specialist visit in the 12 months preceding administration of the survey. Of these, 22% reported difficulty accessing specialist care. Those with difficulties were more likely to be immigrants, post-secondary educated, and have one or more chronic conditions. People living in urban health regions were also more likely to report difficulties accessing care. Primarily wait times were cited as reasons for these difficulties, followed by a perceived lack of availability. CONCLUSIONS: There are difficulties faced by the general population as a whole (e.g., wait times) as well as particular difficulties experienced more frequently by certain groups (e.g., transportation, language, and cost barriers for newcomers). These issues are important, as they may discourage individuals from using necessary health care services, and may contribute to feelings of dissatisfaction with the health care system.
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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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».