Developing an integrated performance management and measurement system in healthcare organisations: a Canadian case study
Bibliographic record
Abstract
This study proposes an approach for developing or improving performance management and measurement systems (PMMSs) for healthcare organisations. First, data is collected to analyse and understand the current organisation's performance management system. Second, the SWOT (Strengths, Weaknesses, Opportunities, Threats) method is used to identify the main aspects of the performance management system to be improved. Third, based on the scientific literature and SWOT analysis, BSC principles are integrated to this performance management system to better align the organisation's performance objectives and indicators with its strategy. Finally, we develop a performance indicator structure and select indicators to be used as well as how these indicators could be integrated and shared with higher hierarchical levels in the organisation by using AHP (Analytic Hierarchy Process). Our approach is applied to the CIUSSS du Centre-Sud-de-l'île-de-Montreal (CCSMTL), a large healthcare network, in the province of Québec, Canada.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".