Bibliographic record
Abstract
Discussion on implementation of the Excellent Care for all Act, 2010 (ECFA Act), Bill 46, has focused on the hospital sector in Ontario, but it also has relevance outside the hospital setting. As primary healthcare, long-term care and home care all receive public funding, these sectors should be expected to be compliant with Bill 46. But does the act also govern government-funded (i.e., by other than the Ministry of Health and Long-Term Care) community-based programs such as adult day programs, meals-on-wheels, nutrition programs for children, and more? We propose that we cannot exclude any of these essential programs. We also consider the non-hospital sector and health organizations that do not receive public funding. The healthcare system will be well served if we consider whether the EFCA Act's key elements should be implemented across the system both vertically and horizontally. Vertical implementation in the hospital sector could be followed by primary care, home and community care, long-term care, and the rest of the vertical silos within the healthcare system. But by taking the horizontal approach, all sectors within and outside of what we traditionally think of health would be integrated using an evidence-informed and outcome-based approach and methodology.
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 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 | 0.027 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.023 | 0.024 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".