Use of Performance Data by Mid-Level Hospital Managers in Ontario: Results of a Province-Wide Survey and a Comparison with Hospital Managers in Europe
Bibliographic record
Abstract
This paper provides insights into the use of performance data by middle managerial staff in Ontario hospitals in 2019 and compares the results to a study conducted in Europe in the same year. A total of 236 managers working in 61 hospitals across Ontario provided responses to the survey. Compared to their European colleagues, Ontario respondents self-assessed using significantly more performance data for managerial decision making. The use of performance data in Ontario was mostly motivated by external accountability requirements, followed by internal quality improvement efforts. Ontario managers also reported accessibility, appropriateness and timeliness of data and human resources and engagement as the biggest barriers to further performance data utilization. Comparative studies, such as the one this paper is based on, provide the foundation for drawing lessons across jurisdictions. This paper also affirms the importance of hospital middle management in moving from quality assurance to quality improvement efforts and developing sustainable learning healthcare organizations and systems.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".