Lessons from mandated implementation of a performance management system
Bibliographic record
Abstract
PURPOSE: Lean-inspired approaches and performance management systems are being implemented in public healthcare organisations internationally. However, the literature is inconclusive regarding the benefits of these management tools and there is a lack of knowledge regarding processes for large-scale implementation of these tools. This article aims to describe the implementation process and to better understand how this process influences the mandated performance management system. DESIGN/METHODOLOGY/APPROACH: = 30). Analysis is based on a sociotechnical approach to management tools that considers organisational context, and the tool's technical substrate, theory of action and managerial philosophy. FINDINGS: Results show that despite a standardised national mandate, the tool as implemented varied between organisations in terms of technical substrate and managerial philosophy. These variations are explained by the flexibility of the technical substrate, the lack of clarity of the managerial philosophy, and some contextual elements. Successful implementation may rest upon high hybridization of the tool on these different dimensions. A precise and prescribed technical substrate is not sufficient to guarantee implementation of a managerial philosophy. PRACTICAL IMPLICATIONS: Mandated implementation of management tools may be more successful if it is explicit on the managerial philosophy, the technical substrate and the link between the two, and if it provides some leeway to adapt both to the organisational context. ORIGINALITY/VALUE: This is one of the few studies to describe and analyse the process involved in mandated large-scale implementation of performance management systems in public healthcare organisations.
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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 | 0.091 | 0.127 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".