Putting Performance Measurement Recommendations into Practice: Building on Current Practices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it