How Do Professional Associations Influence Health System Transformation? Lessons from Ontario, Canada
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.040 | 0.012 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".