LONG-TERM CARE QUALITY IN ONTARIO AND BRITISH COLUMBIA: EXAMINING THE ROLE OF CHARITABLE DONATIONS, FINANCIAL VULNERABILITY, AND FACILITY CHARACTERISTICS
Notice bibliographique
Résumé
For-profit ownership of long-term care homes has long been contentious in Canada and abroad, with concerns that excessive cost-cutting may negatively impact the quality of care. These concerns re-emerged during the COVID-19 pandemic, during which for-profit ownership was associated with larger outbreaks and more deaths in Canada. Existing international research on long-term care quality has suggested that for-profit homes provided worse quality based on multiple measures such as risk-adjusted health outcomes, staffing levels, process indicators, and inspections infractions. One potential explanation is the cost-cutting behaviours of for-profit homes, as they have been reported to provide fewer hours of direct care and substitute cheaper forms of nursing care. In Canada, an additional explanation may have been that not-for-profit homes and public homes had additional revenues such as municipal funding and charitable donations that were not available to most for-profit homes. Despite the controversies, few studies in Canada have examined whether long-term care home ownership status is associated with differences in quality metrics. Only one study from Ontario was identified that used a composite measure of risk-adjusted quality indicators and reported that for-profit homes and not-for-profit homes performed better than municipal homes. This study used data from Ontario and British Columbia, provinces with different funding models, to examine quality based on two outcomes consistent with the literature: the Canadian Institute for Health Information’s (CIHI) publicly reported risk-adjusted long-term care quality indicators, and infractions identified during inspections. There were therefore two main objectives. First, the study examined whether financial vulnerability and charitable donations were associated with differences in quality between private not-for-profit homes, leveraging tax data from the Canadian Revenue Agency. Second, the study examined whether ownership status and other facility characteristics were associated with differences in quality. Results suggested that neither financial vulnerability nor charitable donations were associated with differences in quality. Findings related to ownership status were inconsistent. Private for-profit and private not-for-profit ownership was associated with better performance on the CIHI quality indicators but were also associated with more inspection infractions and complaints compared to public homes. Further research is needed to better elucidate the mechanisms for differences in quality, and to examine whether the differences in CIHI indicator performance reflected true differences in quality or limitations of risk-adjustment.
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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 | 0,003 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,010 |
| Études des sciences et des technologies | 0,006 | 0,002 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,005 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».