Priority measures for publicly reporting primary care performance: Results of public engagement through deliberative dialogues in 3 Canadian provinces
Bibliographic record
Abstract
OBJECTIVE: While public reporting of hospital-based performance measurement is commonplace, it has lagged in the primary care sector, especially in Canada. Despite the increasing recognition of patients as active partners in the health-care system, little is known about what information about primary care performance is relevant to the Canadian public. We explored patient perspectives and priorities for the public reporting of primary care performance measures. METHODS: We conducted six deliberative dialogue sessions across three Canadian provinces (British Columbia, Ontario, Nova Scotia). Participants were asked to rank and discuss the importance of collecting and reporting on specific dimensions and indicators of primary care performance. We conducted a thematic analysis of the data. RESULTS: Fifty-six patients participated in the dialogue sessions. Measures of access to primary care providers, communication with providers and continuity of information across all providers involved in a patient's care were identified as the highest priority indicators of primary care performance from a patient perspective. Several common measures of quality of care, such as rates of cancer screening, were viewed as too patient dependent to be used to evaluate the health system or primary care provider's performance. CONCLUSIONS: Our findings suggest that public reporting aimed at patient audiences should focus on a nuanced measure of access, incorporation of context reported alongside measurement that is for public audiences, clear reporting on provider communication and a measure of information continuity. Participants highlighted the importance the public places on their providers staying up to date with advances in 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 | 0.071 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.029 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| 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".