Bibliographic record
Abstract
First, I conducted a hospital survey of 160 Ontario hospital Chief Executive Officers (CEOs) asking CEOs to self report on the fairness of their priority setting. Eight-six CEOs completed the survey (54%). 60.7% of respondents indicated their hospitals' priority setting was fair. With respect to A4R conditions, respondents indicated their hospitals performed best for 'relevance' (75.0%), followed by 'revision/appeals' (56.6%), 'publicity' (56.0%), and 'enforcement' (39.5%) conditions. Greatest room for improvement existed in meeting the 'enforcement' condition. This study expands on the likely relationship between leadership and priority setting. The overall aim of this research was to describe, evaluate and identify opportunities to improve priority setting in Ontario hospitals using Daniels and Sabin's ethical framework of 'accountability for reasonableness' (A4R). Second, I conducted interviews with 46 CEOs of Ontario hospitals, and developed a framework of leadership characteristics in hospital priority setting, involving: vision, alignment, relationships, values and process. The fledgling framework developed in this research provides, I believe, a leadership guide for decision makers to improve the quality of their leadership, and in so doing, fairness of their priority setting. Overall, the most valuable innovation of this research was the finding of an alignment between leadership concepts and concepts of ethical priority setting. Third, I conducted a policy analysis concerning the implementation of Ontario hospital accountability agreements, evaluated against A4R. Analysis suggested that government only partially met the 'relevance' condition however there was evidence of social learning and movement towards the establishment of inclusive stakeholder arrangements. Evidence suggested government eventually progressed towards meeting the 'publicity' condition. Government only partially met the 'revision/appeals' condition. Government did not meet the 'enforcement' condition, as the other conditions were only partially met. It is my view that regional governance structures in Ontario (i.e. Local Health Integration Networks or LHINs) provide an opportunity for the province to improve the fairness of their accountability agreement processes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".