The impact of hospital language on the rate of in-hospital harm. A retrospective cohort study of home care recipients in Ontario, Canada
Notice bibliographique
Résumé
BACKGROUND: Patients who live in minority language situations are generally more likely to experience poor health outcomes, including harmful events. The delivery of healthcare services in a language-concordant environment has been shown to mitigate the risk of poor health outcomes related to chronic disease management in primary care. However, data assessing the impact of language-concordance on the risk of in-hospital harm are lacking. We conducted a population-based study to determine whether admission to a language-discordant hospital is a risk factor for in-hospital harm. METHODS: We used linked administrative health records to establish a retrospective cohort of home care recipients (from 2007 to 2015) who were admitted to a hospital in Eastern or North-Eastern Ontario, Canada. Patient language (obtained from home care assessments) was coded as English (Anglophone group), French (Francophone group), or other (Allophone group); hospital language (English or bilingual) was obtained using language designation status according to the French Language Services Act. We identified in-hospital harmful events using the Hospital Harm Indicator developed by the Canadian Institute for Health Information. RESULTS: The proportion of hospitalizations with at least 1 harmful event was greater for Allophones (7.63%) than for Anglophones (6.29%, p < 0.001) and Francophones (6.15%, p < 0.001). Overall, Allophones admitted to hospitals required by law to provide services in both French and English (bilingual hospitals) had the highest rate of harm (9.16%), while Francophones admitted to these same hospitals had the lowest rate of harm (5.93%). In the unadjusted analysis, Francophones were less likely to experience harm in bilingual hospitals than in hospitals that were not required by law to provide services in French (English-speaking hospitals) (RR = 0.88, p = 0.048); the opposite was true for Anglophones and Allophones, who were more likely to experience harm in bilingual hospitals (RR = 1.17, p < 0.001 and RR = 1.41, p < 0.001, respectively). The risk of harm was not significant in the adjusted analysis. CONCLUSIONS: Home care recipients residing in Eastern and North-Eastern Ontario were more likely to experience harm in language-discordant hospitals, but the risk of harm did not persist after adjusting for confounding variables.
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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 | 0,004 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».