How Do Professional Associations Influence Health System Transformation? Lessons from Ontario, Canada
Notice bibliographique
Résumé
Introduction: Internationally, there are increasing collaborative efforts to transform health care systems in ways that align with the principles of integrated care. Within the Canadian context, the current province-wide reorganization of health care into Ontario Health Teams (OHTs) is an example of a large-scale system transformation, requiring collaboration between system stakeholders at macro-, meso- and micro-levels. Literature on the topic inter-organizational collaboration does not fulsomely consider the role of local professional associations in health system transformations. There is value in examining the role of professional associations in influencing health system transformations, as they have significant expertise and influence, particularly in the domain of health policy. Methods: We used a qualitative descriptive approach to explore the research question: what strategies have local professional associations used to influence the development of Ontario Health Teams? We collected data from eight, 60-minute interviews with senior level leaders from eight professional associations. Interviews were transcribed and data were analyzed using content analysis to construct four descriptive themes. Results: During times of health system transformation, professional associations balance the functions of: (1) supporting members, (2) negotiating with government, (3) collaborating with stakeholders, and (4) reflecting on their role. In this presentation, we focus primarily on themes #2 and #3, given the specific relevance of governance, leadership, and collaboration. In both themes, participants emphasized the importance of findings areas of alignment with the government and other stakeholders; and using shared aims as way to amplify their influence. Characteristics of a “good” collaborator included consistent demonstration of mutual respect, clear communication, and interest in building trusting relationships. Participants also described barriers to collaboration, such as power imbalances and lack of role clarity. These challenges were heightened within the context of the “low rules” OHT rollout, in which OHTs had a high degree of control in adapting their team structures to ideally meet the needs of their local care partners and patient population(s), Conclusion and Implications: Professional associations are highly connected groups, deeply engaged with their members (often frontline clinicians) and regularly engaged with other key stakeholders and decision-makers (e.g., government). PAs play a critical role in influencing health system transformations, by bringing forward practical solutions to government that reflect the needs of their members, often frontline clinicians. Sharing insights from this work with an international audience could support global dialogues with health system leaders, policymakers, and researchers about leveraging the strengths of professional associations to enhance large-scale health system transformations via strategic collaboration. Next Steps: This research was conducted to fulfill the requirements of the Health System Impact Fellowship (doctoral level, funded by the Canadian Institutes of Health Research). We have continued to engage selected professional associations in building a collaborative framework, grounded in their experiences of participating the development of Ontario Health Teams.
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 | 0,009 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,040 | 0,012 |
| Communication savante | 0,008 | 0,003 |
| Science ouverte | 0,003 | 0,007 |
| Intégrité de la recherche | 0,002 | 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 ».