A Framework for Performance Management of Clinical Practice
Bibliographic record
Abstract
In this paper, we present a framework for performance management of clinical practice. The framework defines a performance management participation model, which identifies the processes that need to be managed at the micro, meso, and macro levels for a clinical practice, and which identifies the key actors and tasks relevant to performance management. It defines a performance measurement model, which maps goals and indicators to the performance management participation model. In addition, it includes a methodology for implementation and evaluation of tools that can be integrated into care processes to support the data collection and report notification tasks identified in the performance management participation model. We revisit the case study of implementing a resident practice profile app, in light of the proposed framework, to support performance management of a family health practice. We demonstrate how the framework is useful for explaining why the use of the app was abandoned after two years of its introduction and, therefore, how the framework is an improvement to our previous methodology for development of performance management apps for clinical practice.
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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 | 0.064 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".