Canadian Chief Executive Officers' Prescription for Higher Quality: More Clinical Engagement, Shared Accountability and Capacity Development
Bibliographic record
Abstract
uality of Healthcare in Canada: A Chartbook, published by the Canadian Health Services Research Foundation (CHSRF) in 2010, showed that when compared with other developed countries, Canada ranks in the middle of the pack with respect to healthcare quality. How we can best achieve higher-quality care is a much-debated issue in Canada and in most modern healthcare systems – and there are many conflicting views. The historical guild of professionalism through selfregulating professional colleges remains intact, especially for issues of poor conduct or criminal behaviour. However, there is widespread evidence of errors of commission (e.g., wrong-sided surgery or medication error) and omission (e.g., under-managed care or infection caused by an absence of hand hygiene) and unacceptable variation in quality of care (CHSRF 2011). So beyond the regulatory bodies, important new organizational, managerial and legislative imperatives are emerging to improve quality. With the emergence of “accountable care organizations” in the United States and new legislation in several Canadian provinces promising greater attention to performance measurement and reporting, there is increasing movement toward enhancing accountability structures and mechanisms as a means of achieving higher-quality care. To promote further examination of accountability for quality, CHSRF, in partnership with the Association of Canadian Academic Healthcare Organizations, the Canadian Institute for Health Information and the Canadian Medical Association, chose Leadership Accountability in Canadian Healthcare: Creating the Momentum to Improve Quality as the theme of the fifth annual CEO Forum in Montreal on February 16, 2011. Over 150 leaders from various sectors of Canada’s healthcare system were in attendance at the forum. As part of the program, a polling exercise was conducted to gain participants’ perspectives on quality and to promote discussion.
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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".