Identifying Canadian patient-centred care measurement practices and quality indicators: a survey
Bibliographic record
Abstract
BACKGROUND: Patient-centred quality indicators allow health care systems to monitor and evaluate patient-centred care practices and identify gaps in health care quality. Our objective was to determine whether Canadian provinces and territories measure patient-centred care, identify patient-centred quality indicators currently being used and compare patient-centred care practices and measurement in Canada to those of health care systems in other countries. METHODS: An online survey was developed to collect data on demographic characteristics, patient-centred care practices, and indicators used at quality improvement organizations and health care authorities. The survey was conducted with quality improvement leads in Canada and 4 other countries. Content analysis methods were used to analyze and report the data. Patient-centred quality indicators were identified and categorized according to the Donabedian framework (structure, process, outcome). RESULTS: The survey had a response rate of 47/67 (70%) and a completion rate of 58/60 (97%). We obtained completed surveys from 12 of the 13 provinces and territories in Canada. Respondents from most provinces indicated their organization used patient-centred care measures to inform practices. Respondents in only 4 provinces/territories reported using patient-centred quality indicators, for a total of 61 unique indicators. Most indicators used across Canada assessed aspects of care related to the Donabedian components of process and outcome. Findings for Canada were comparable to those for Sweden, England, Australia and New Zealand, where many measures are still in development. INTERPRETATION: This study provided greater insight into patient-centred care measurement across Canada, Sweden, England, Australia and New Zealand and helped us to identify patient-centred quality indicators currently in use. These results will inform the development of a standard set of patient-centred quality indicators for implementation by health care organizations to improve the quality of health care.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".