Measuring Lessons Learned from Durham Community Hub Model during COVID-19: A Support Solution for the Homeless and other Vulnerable Populations
Notice bibliographique
Résumé
Introduction/background: The COVID-19 pandemic significantly impacted the availability of community support and resources to vulnerable populations, including persons experiencing homeless within the Regional Municipality of Durham, Ontario, Canada. In response, the Region developed pilot community hubs to address escalating unmet housing, healthcare and support needs. Why did you do it? The community hub evaluation project is a community-based research co-created by a research team from Ontario Tech University and community stakeholders to measure the community hubs' effectiveness in addressing service users' unmet needs during the COVID-19 pandemic. Who is it for? The project is relevant to healthcare and social service policymakers and service providers, service users, researchers, and community organizers involved in researching, funding, designing and delivering healthcare and social services for vulnerable populations. Who did you involve and engage with? The project collaborators comprised a research team from Ontario Tech University and an advisory committee of community hub staff members and service users convened by the research team. All aspects of the project’s design and implementation, including creating the data collection tools, participant recruitment, data collection, data analysis and developing the knowledge translation products, were completed in collaboration with the research advisory committee that met monthly. Service users on the advisory committee were compensated for their time and contribution. What did you do? A mixed-methods research design was used to explore the experiences of the community hub service users, staff members and subject matter experts. Service users were recruited from the community hubs. Staff members were recruited through virtual staff meetings. Face-to-face surveys were administered to 75 service users at the community hubs. Staff online surveys were self-administered by 15 direct service staff. Virtual interviews were conducted with five community hub managerial staff and two subject matter experts. Quantitative data were calculated using the SPSS statistical software. Qualitative data were analyzed using thematic analysis. What results did you get? What impact did you have? The integrated service model was described as saving service users’ lives. Adequate, core funding is required for the sustainability of the community hubs. Results from the evaluation provided the regional government with data to help inform its strategic priorities for vulnerable populations. What is the learning for the international audience? The needs of vulnerable populations including persons experiencing homelessness are deep and entrenched. Community hubs integrate a range of healthcare and social support services, including the social determinants of health that respond to service users’ immediate and emerging needs. What are the next steps? Knowledge translation through conferences, symposiums, presentations at regional sector tables and peer-reviewed publications. Collaborating with the community hubs to utilize the research findings to support improvement in future service design and delivery.
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 | 0,018 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,010 | 0,004 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,004 | 0,007 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 ».