Bibliographic record
Abstract
Accountability is being stressed in the Canadian health care environment. This paper uses a framework that considers both the production characteristics of services provided and the type of accountability sought, and how they may impact a policy tools’ ability to achieve accountability. The production characteristic focused on is 'measurability,' or more specifically, the performance measures currently being used in the province of Ontario to achieve accountability. These measures are considered alongside the criteria of a high performing health system and policy tools, and are compiled into an inventory. Whether these accountability or performance measures align with the criteria of a high-performing health system may influence the likelihood that accountability for these criteria is achieved using the available policy tools. The inventory of available measures helps identify criteria, such as patient satisfaction and health promotion/population health, which are challenging to assess. In the case of patient satisfaction, a large number of measures were used to deal with the challenge of assessing performance. Conversely, health promotion/population health has only one corresponding measure. Health system efforts to achieve accountability are commendable, even if imperfect. These results indicate the opportunity for further research around accountability and the creation of measures.
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 | 0.154 | 0.294 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.024 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 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".