Factors influencing use of a performance measurement system in a rehabilitation hospital
Bibliographic record
Abstract
Background: Rehabilitation hospitals, like other healthcare organizations, are under increasing pressure to apply management tools such as performance measurement systems (PMSs). PMS implementation represents a major challenge as it involves significant organizational change. This study explored how a PMS was used in a rehabilitation hospital and what were the factors explaining its use.Methods: A qualitative longitudinal study was conducted. Two data sources were used: interviews with hospital directors and organizational documents. Semi-structured interviews were conducted pre-implementation (n = 7) and 10 months post-implementation (n = 7) of the PMS. A total of 111 documents produced between 2011 and 2014 were reviewed. The Consolidated Framework for Implementation Research (CFIR) was used as a conceptual framework for data collection and analysis.Results: Decision makers used the PMS mostly for monitoring and accountability purposes and also for promoting their organization’s performance and enhancing the organization’s credibility. It was rarely used to trigger change management projects. Major barriers to PMS use were the lack of planning of the implementation process, available resources and the perceived quality of the developed PMS. Major facilitators for PMS implementation were related to continuous leadership engagement, specific PMS characteristics (perceived advantages, lack of complexity), quality of communications, and a perceived need for a PMS. Some key recommendations are proposed to decision makers that may enhance PMS use.Conclusions: PMS use is influenced by multiple factors, however the positive or negative influence of each factor is context dependent. Key recommendations are proposed to decision makers that may enhance PMS use.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".