Cross-Sectoral Collaboration to Improve Outcomes for Children/Youth in Vulnerable Contexts: Policy Dialogue Report
Notice bibliographique
Résumé
Executive Summary This report summarizes a one-day policy dialogue event that brought together 55 stakeholders and persons with lived experiences to discuss key lessons from TRiP (The Regina intersectoral Partnership) initiative. TRiP is an example of sustained collaboration across six human service organizations to improve outcomes for children and youth in vulnerable contexts within Regina, Saskatchewan, since 2010. The event aimed to explore how these lessons can be applied in other intersectoral contexts through the engagement of the participants in 1) TRiP translation and 2) consensus-building activities (World Café). During TRiP translation group activity (see details below), participants were asked about the top reason for TRiP's success. Collaboration was the most common response among 28 participants, followed by the knowledge and dedication of frontline staff (4 participants) and shared consent (2 participants). When asked what aspects of TRiP could be translated into other contexts, participants identified six key categories: effective communication, governance and leadership, building relationships and trust, accountability and responsibility, evaluation and measurement, and organization support and resources. During the World Café conversations, the event participants discussed the core themes that emerged from the research study including governance and leadership, accountability, information sharing, defining and measuring success and resources. Below is the summary of key findings in each theme. Governance and Leadership: Participants emphasized the need for buy-in from government and higher-level leadership, suggesting concrete actions beyond written strategies to foster cross-sectoral collaboration. They discussed various leadership models, including single-entity and shared approaches, with considerations for accountability and alternative governance structures. Accountability: Challenges to accountability in collaborative initiatives were identified, including a lack of shared definitions and siloed structures hindering collaboration. Proposed solutions included inclusive engagement strategies, enhanced communication, capacity building, and person-centered care for improved service continuity. Information Sharing: Participants stressed the importance of building trust among partners, understanding sector skill sets, and utilizing shared physical space for efficient collaboration. Purposeful information collection and sharing, with a trauma-informed approach, empower clients and improve service delivery. Defining and Measuring Success: Defining success in collaborative initiatives such as TRiP was seen as complex, tailored to individual needs, and requiring a holistic approach with quantitative and qualitative measures. Success was viewed as collective and reflective of strong partnerships and family connections. Resources: Concerns about potential burnout among dedicated staff and financial challenges, especially in securing government funding, were noted. Suggestions included exploring direct resource allocation options and addressing the high turnover rate among TRiP personnel to enhance service 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 | 0,056 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,006 | 0,001 |
| Communication savante | 0,013 | 0,006 |
| Science ouverte | 0,003 | 0,014 |
| Intégrité de la recherche | 0,007 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,027 | 0,005 |
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 ».