Responding to COVID-19 with integrative health and sheltering models for persons experiencing homelessness in Canada: A case study exploring implementation
Notice bibliographique
Résumé
Introduction: Throughout the COVID-19 pandemic, persons experiencing homelessness in Canada were disproportionately put at high risk for infection and adverse health outcomes, which prompted many regions to respond by adopting integrative health and sheltering programs to address the unique health inequities faced by this population. Objectives: We aimed to deepen our understanding of the experiences and processes related to the adoption of such integrative health and sheltering programs by exploring the implementation and sustainability of two regional responses in Southern Ontario. Methods: Our study used a multiple case-study design to investigate how different regions responded and implemented integrative health and sheltering models during the pandemic within two mid-sized cities in Southern Ontario. Members of our research team participated in iterative consultations with relevant stakeholders from local agencies and organizations to ground the study’s objectives, data collection strategies, and interview guides in community-based feedback. Using a purposive sampling strategy, we recruited program staff from varying levels of leadership (i.e., front-line health and social care providers, program managers, regional executives) across both regions (n=16) to participate in semi-structured interviews. Data analysis occurred in four stages: 1) narrative descriptions of each site were created; 2) interview transcripts were coded using NVivo 14 and analyzed by members of the research team through line-by-line emergent coding to identify main themes; 3) main themes were cross-referenced and aligned with domains from the Consolidated Framework for Implementation Research (CFIR) to enrich understanding; and 4) analysis culminated in cross-case comparison between sites. Results and impact: The CFIR-informed coding generated multiple themes which aligned with the domains of the framework (innovation, outer setting, inner setting, individuals, and implementation process). Each thematic domain was organized based on implementation barriers and facilitators. Notable barriers included: poor integration between service sectors; limited funding and staffing resources; and differing organizational philosophies. Whereas key facilitators included: trusting relationships amongst partners and staff; prioritizing immediate solution-oriented approaches, especially given the context of the pandemic; and breaking down silos through a shared sense of responsibility. In addition to the CFIR-informed thematic domains, emergent coding further captured themes which spoke to the unique contextual impact of the pandemic on the implementation process. Lessons learned: Although integrative health and sheltering programs were implemented to reduce disease transmission among persons experiencing homelessness during a global health crisis, uniting both health and social care in a collaborative model consequently fostered a shared sense of responsibility across diverse stakeholders. This highlighted the importance of simultaneously targeting multiple social determinants of health to improve overall health outcomes for this population. Integrative health and sheltering models have the potential to consolidate and thereby strengthen funding, personnel support, and service delivery for persons experiencing homelessness beyond the pandemic, which can work towards dismantling health equities faced by this population and advancing efforts to addressing this longstanding public health issue in other countries amid the fallout of COVID-19. Next steps: Using our findings, we will collaborate with project partners to co-design implementation guides for other regions interested in developing similar integrative programs.
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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 | 0,004 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,027 | 0,007 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,004 |
| 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 ».