Governance for Quality and Patient Safety: The Impact of the Ontario Excellent Care for All Act, 2010
Bibliographic record
Abstract
The passage of the Excellent Care for All Act, 2010 (ECFA Act) in Ontario has confirmed the responsibilities of hospital boards for quality of care and reinforced expectations that they will monitor performance and establish strategic aims in this area. Quality of care and patient safety have created a new agenda for many healthcare boards that had only a limited focus on these issues. Here, we report on interviews with five Ontario healthcare organizations identified by experts as having high-performing boards. Our question was, how has the ECFA Act influenced Ontario healthcare organizations' governance practices relating to quality and safety? While the act has raised the profile of these issues, in the short-term it may have blunted the effectiveness of some boards that had already developed a clear strategic focus on quality and patient safety. Executive compensation was the most contentious issue; the introduction of pay for performance was considered poor timing, given the Ontario government's pay freeze. Overall, the act is an important step in increasing responsible governance and has helped align governance activities with the core work of hospitals--delivering high-quality care. However, effective policy must create an environment where all organizations focus on improvement, but where regulation does not limit the capabilities of leading organizations to achieve even higher performance.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".