What do hospital decision-makers in Ontario, Canada, have to say about the fairness of priority setting in their institutions?
Bibliographic record
Abstract
BACKGROUND: Priority setting, also known as rationing or resource allocation, occurs at all levels of every health care system. Daniels and Sabin have proposed a framework for priority setting in health care institutions called 'accountability for reasonableness', which links priority setting to theories of democratic deliberation. Fairness is a key goal of priority setting. According to 'accountability for reasonableness', health care institutions engaged in priority setting have a claim to fairness if they satisfy four conditions of relevance, publicity, appeals/revision, and enforcement. This is the first study which has surveyed the views of hospital decision makers throughout an entire health system about the fairness of priority setting in their institutions. The purpose of this study is to elicit hospital decision-makers' self-report of the fairness of priority setting in their hospitals using an explicit conceptual framework, 'accountability for reasonableness'. METHODS: 160 Ontario hospital Chief Executive Officers, or their designates, were asked to complete a survey questionnaire concerning priority setting in their publicly funded institutions. Eight-six Ontario hospitals completed this survey, for a response rate of 54%. Six close-ended rating scale questions (e.g. Overall, how fair is priority setting at your hospital?), and 3 open-ended questions (e.g. What do you see as the goal(s) of priority setting in your hospital?) were used. RESULTS: Overall, 60.7% of respondents indicated their hospitals' priority setting was fair. With respect to the 'accountability for reasonableness' conditions, respondents indicated their hospitals performed best for the relevance (75.0%) condition, followed by appeals/revision (56.6%), publicity (56.0%), and enforcement (39.5%). CONCLUSIONS: For the first time hospital Chief Executive Officers within an entire health system were surveyed about the fairness of priority setting practices in their institutions using the conceptual framework 'accountability for reasonableness'. Although many hospital CEOs felt that their priority setting was fair, ample room for improvement was noted, especially for the enforcement condition.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".