ISQUA17-1692NO SECTOR LEFT BEHIND: ADVANCING MENTAL HEALTH QUALITY IN ONTARIO, CANADA
Bibliographic record
Abstract
Sixteen years ago, Ontario embarked on a process of improving measurement systems and use of information to improve the cancer system and population-based cancer outcomes. [1] Three years later, Ontario developed a Wait Times Strategy that used a similar focus on measurement and public reporting to drive performance improvement. [2] This practice was further entrenched through the establishment of a provincial advisor on health care quality (Health Quality Ontario) that reports to the people of Ontario on how well the health system is performing, including recent reporting on long-term care and health system quality. Not surprisingly, progress in access and policy was observed in those areas where reporting and performance were developed and emphasized. [3] However, for much of the last two decades that have seen significant progress in health system performance measurement and service delivery, Ontario's performance reporting has been silent on the quality of services and care received by people living with mental illness and addictions, despite the evident need to address the challenges in mental health service delivery. [4]
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".