The beginnings of health system transformation: How Ontario Health Teams are implementing change in the context of uncertainty
Bibliographic record
Abstract
PURPOSE/ SETTING: The launch of Ontario Health Teams (OHTs) by the Canadian province of Ontario in 2019 represented a milestone in the journey towards integrated care and population health management. However, early model development was riddled with uncertainty. We explore what makes transformation possible even in the context of uncertainty. METHODS: We conducted 125 interviews with administrators, clinicians, and patient and family advisors across 12 OHTs, representatively selected across geography and leadership sector, between January to September 2020. Interviews were transcribed and thematically coded, and a Foucauldian approach informed analysis. FINDINGS: A sense of uncertainty was identified at three levels: (a) at a cross-organizational level, policymakers were perceived as providing inadequate direction; (b) at a sectoral level, certain sectors were uncertain about participating due to historic vulnerabilities; and (c) at a professional level, physicians were uncertain about the value of the new model and their place within it. These concerns were countered by a recognition of the need for change, inclusive decision-making, and developing empathy and awareness of each other's needs. This helped unsettle traditional hierarchies and facilitate new forms of certainty. CONCLUSION: Understanding the possibilities and challenges of this endeavour will be helpful to program implementers negotiating uncertain environments as well as to policymakers seeking to provide guidance without stymieing local innovation.
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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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.027 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".