Aligning goals of for-profit, not-for-profit, and public healthcare organizations by governing for quality: A model for change in the U.S. and other jurisdictions
Bibliographic record
Abstract
There has been widespread criticism of privately owned or operated healthcare organizations in Canada and beyond. However, governments have limited resources to infuse the capital and provide the scale necessary to rapidly address the post-pandemic needs of healthcare systems. Ensuring that healthcare providers regardless of ownership or for-profit or not-for-profit status, provide high quality care and ensure health equity is paramount. Here, we propose the use of a governance for quality model based on the Excellent Care for All Act (2010) developed for public hospitals in Ontario for all healthcare organizations regardless of ownership or profit status, to better align all forms of healthcare providers with quality outcomes and equitable and positive patient experience. We believe that this framework is applicable to healthcare organizations both public and private, for-profit and not-for-profit in Canada, the U.S. and beyond.
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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 | 0.036 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.039 |
| Scholarly communication | 0.022 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.008 | 0.009 |
| 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".