COVID-19 Community Response Team for Toronto Homeless Services and Congregate Living Settings: an evaluation of Hospital-Community partnership through COVID-19 vaccine provision
Notice bibliographique
Résumé
Individuals experiencing homelessness face unique physical and mental health challenges, increased morbidity and premature mortality. In Canada, it is estimated that 235,000 individuals experience homelessness annually, and 180,000 use emergency shelters each night. (1) COVID -19 creates a significant heightened risk for those living in congregate sheltering spaces. Individuals with a recent history of homelessness and diagnosed with COVID-19, are at significantly higher risk of hospitalization and death than those housed in Ontario communities (2). Women’s College Hospital (WCH) is an ambulatory hospital situated in downtown Toronto. In March 2020, WCH set up one of Toronto’s 14 COVID-19 assessment centres to facilitate free testing for SARS-CoV-2. Formed by a group of health care providers at WCH, the goal of the COVID-19 Community Response Team (CRT) was to support Toronto shelters and congregate living sites to manage and prevent outbreaks of SARS-CoV-2 using a collaborative model through onsite mobile testing; supporting the management and prevention of outbreaks; and providing infection prevention and control training and guidance. (3) In total, CRT leveraged this model of care with 49 shelter and congregate living sites from April 2020 to April 2021. From this, the WCH COVID-19 vaccine program emerged, where 14 shelters were regionally identified to co-design and support the administration of vaccine clinics within each sheltering site. This research seeks to evaluate the impact and importance of this partnership model and its future potential in community-centered integrated care. In this study, three areas of inquiry are addressed: (1) Vaccine program evaluation and lessons learned (e.g., What were barriers, facilitators, and lessons throughout the process? How were shelter staff and clients impacted?); (2) Perceptions on hospital/community partnership (e.g., What were overall perceptions of this partnership and strategy?); (3) Opportunities forward (e.g., How can this partnership between hospitals and shelters be sustained in the future to fulfill needs beyond COVID-19). Constructivist grounded theory (CGT) is used in this project to explore perceptions and experiences of this partnership. (4) CGT data analysis revealed five main categories, 16 subcategories, and one core category. The core category is “access to healthcare is a human right; understand our communities”. The main categories are COVID-19 response capacity, outbreak identification and management, barriers to the vaccine program, community-centred immediate shelter needs, and avenues for intersectoral relationship strengthening. In conclusion, three key takeaways emerged for health(care) policy and practice: 1.‘Health as a human right’ framework is an organizing principle in shelters but not necessarily in hospitals. How can hospitals adopt and integrate this framework at the policy level to operationalize an equity-based approach to care? 2.For hospitals, there are gaps in knowledge about community and shelter realities. Ongoing formal partnering between hospitals and communities is one way to bridge this gap. 3.Empowering shelter staff is crucial to the success of hospital-partnered programs and clinical interventions. Finally, this project calls attention to the urgent context-specific exploration needed to advance official hospital-community partnerships, where there is an everlasting commitment and accountability.
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 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 | 0,014 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».