Strategies Ontario Hospital Administrators Apply to Generate Non-Government Revenue to Remain Sustainable
Bibliographic record
Abstract
Health care administrators in Ontario want to transform health care with a focus on improving efficiency and quality of care, yet they pay little attention to increasing revenue. The purpose of this qualitative case study was to explore strategies Ontario hospital administrators apply to generate nongovernment revenue to remain sustainable. The target study population consisted of 2 chief executive officers and 2 chief financial officers at Ontario academic research hospitals. The conceptual framework for this study included radical organizational change theory supported by complexity leadership theory, and grounded in an evidence-based approach. The researcher conducted open-ended semi-structured interviews and made efforts to collect relevant documents. The data analysis process included coding of the interviews followed by identifying themes and aggregate dimensions. Five themes emerged including working within the fiscal reality, the impact of the political environment, the focus on the mission, nongovernment revenue generation, and opportunities for the Ontario academic research hospital. The application of the findings from this study may contribute to social change by encouraging hospital executives to adopt a more coordinated and consistent approach to generating nongovernment revenue to support the mission of their hospitals.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".