Journal of Public Administration Research and Theory Advance Access published December 26, 2006 Accountability Agreements in Ontario Hospitals: Are They Fair?
Bibliographic record
Abstract
Governments can be accountable for improving the fairness of their priority setting through enhanced transparency and stakeholder engagement. A case analysis is conducted of priority setting in a government health care context in Ontario, Canada, assessing how implementation of hospital accountability agreements meets the conditions of a leading international ethical framework for priority setting, ‘‘accountability for reasonableness’’ (A4R). Hospital accountability agreements provide a mechanism for government to ensure that public funding achieves desired performance in hospitals. A key goal of priority setting is fairness. A4R links priority setting, legitimacy, and fairness to theories of democratic deliberation, making a claim for fairness if the four conditions of relevance, publicity, revision/ appeals, and enforcement are satisfied. Regarding the relevance condition, this analysis suggests that government only partially met the relevance condition providing limited stakeholder engagement but with evidence of policy learning and movement toward the establishment of inclusive stakeholder arrangements. Evidence suggests that government eventually progressed toward meeting the publicity condition. Government only partially
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.048 | 0.003 |
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".