ISQUA16-1473PATIENT-CENTRED MEASUREMENT OF EXPERIENCES AND OUTCOMES OF CARE IN BRITISH COLUMBIA, CANADA: STATISTICS WITHOUT THE TEARS WIPED OFF
Bibliographic record
Abstract
To demonstrate how British Columbia (BC), Canada changed its standardized, scientifically rigorous, province-wide approach to measurement and reporting of patient-centred care to ensure data was used locally for quality improvement purposes and provincially to inform policy development. BC's patient-centred measurement program gives those who use healthcare services a voice in improving the quality of the care they receive. BC has collected feedback for 13 years from over one million patients about their perceptions of our healthcare system. An underlying assumption of the program was that routine feedback provided to clinicians about the experiences of “their own” patients would drive improvements. Interviews with key stakeholders explored success factors and barriers to using survey results. Although there was appreciation for methodologically robust data collection and reporting, clinicians and leaders did not want systematic translation of the voice of patients into “statistics with the tears wiped off”. This seemed counterintuitive to a program intended to increase patient-centredness. Cycles of tests of change engaged leaders, staff, and patients with the aim of developing patient-centred, user-friendly, easier to read, and more timely reports.
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.
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 | 0.063 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 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".