Exploring how Professional Associations Influence Health System Transformation: The Case of Ontario Health Teams
Bibliographic record
Abstract
Introduction: Health care system transformations that align with the principles of integrated care require the collaborative efforts of various macro-, meso- and micro-level stakeholders. Understanding the roles of various system actors can improve collaboration in ways that support purposeful health system change. Professional associations (PAs) have considerable influence, but little is known about the strategies they use to influence health system transformation. Methods: Using a qualitative descriptive approach, eight interviews with 11 senior level leaders from local PAs were conducted to learn about the strategies used to influence the province-wide reorganization of health care into Ontario Health Teams. Results: During times of health system transformation, PAs balance: (1) supporting members, (2) negotiating with government, (3) collaborating with stakeholders, and (4) reflecting on their role. The enactment of these various functions demonstrates the strategic nature of PAs, and showcases their ability to evolve in ways that align with the dynamic nature of healthcare. Discussion: PAs are highly connected groups, deeply engaged with their members and regularly engaged with other key stakeholders and decision-makers. 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. PAs strategically seek opportunities for collaboration with stakeholders that can amplify their message. Conclusion: Insights from this work could support health system leaders, policymakers, and researchers in leveraging the role of PAs in health system transformations via strategic collaboration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".