Hospital network performance: A survey of hospital stakeholders’ perspectives
Bibliographic record
Abstract
Hospital networks are an emerging organizational form designed to face the new challenges of public health systems. Although the benefits introduced by network models in terms of rationalization of resources are known, evidence about stakeholders' perspectives on hospital network performance from the literature is scanty. Using the Competing Values Framework of organizational effectiveness and its subsequent adaptation by Minvielle et al., we conducted in 2009 a survey in five hospitals of an Italian network for oncological care to examine and compare the views on hospital network performance of internal stakeholders (physicians, nurses and the administrative staff). 329 questionnaires exploring stakeholders' perspectives were completed, with a response rate of 65.8%. Using exploratory factor analysis of the 66 items of the questionnaire, we identified 4 factors, i.e. Centrality of relationships, Quality of care, Attractiveness/Reputation and Staff empowerment and Protection of workers' rights. 42 items were retained in the analysis. Factor scores proved to be high (mean score>8 on a 10-item scale), except for Attractiveness/Reputation (mean score 6.79), indicating that stakeholders attach a higher importance to relational and health care aspects. Comparison of factor scores among stakeholders did not reveal significant differences, suggesting a broadly shared view on hospital network performance.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".