Putting Performance Measurement Recommendations into Practice: Building on Current Practices
Bibliographic record
Abstract
Improving performance measurement within the Canadian healthcare system is proving to be challenging despite advances in evidence-informed care and best practices for healthcare delivery. Perhaps what is most challenging is the need to meet requirements to measure what most Canadians hold dear - being seen as a person during a healthcare encounter. Measures of healthcare delivery have typically been developed to capture patient satisfaction during isolated healthcare encounters. Such measures simply do not get to the essence of what matters to patients and their families. This paper outlines a response to the paper by Kuluski and colleagues (2017) that calls for a thorough review of the way data are currently captured on patients' experiences with healthcare. Using geriatric medicine as a context, the authors highlight elements of our current care delivery models that must be preserved, modified or created to allow patients and families to play a larger role in improving our healthcare system.
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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 | 0.396 | 0.619 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.030 | 0.036 |
| Open science | 0.018 | 0.018 |
| Research integrity | 0.015 | 0.044 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".