Patient-Centred Measurement in British Columbia: Statistics without the Tears Wiped Off.
Bibliographic record
Abstract
At the heart of every data point in healthcare is a person. British Columbia's (BC) province-wide, coordinated survey program, established in 2002, gives people who use BC's healthcare services a voice in improving the quality of the care and services they receive. Survey data or statistics are presented without the tears wiped off by integrating quantitative results along with a "human" voice or story annotated directly into reports to illustrate the numerical feedback. In this way the data represent the true lived experiences of people who use our healthcare services and allow us to evaluate our progress towards providing truly patient-centred care. After over a decade of measurement and reporting of patient experiences, BC will pioneer a new approach. People who receive healthcare services in BC will be asked to provide feedback across their entire episode of care. And, because routine measurement of patient experiences and patient outcomes in healthcare is a provincial strategic objective, patients will be asked to assess both their experiences of care (patient self-reported experiences) and their outcomes of care (patient self-reported outcomes). This change in measurement strategy builds on 13 years of continuous improvement in patient-centred data collection, reporting and action based on feedback from BC's patients and families.
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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 | 0.050 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.039 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".