Citizen perspectives on the use of publicly reported primary care performance information: Results from citizen‐patient dialogues in three Canadian provinces
Bibliographic record
Abstract
OBJECTIVE: Performance measurement and reporting is proliferating in all sectors of the healthcare system, including primary care, despite a dearth of evidence on how the public uses reports on primary care performance. We explored how the public might use this information, to guide the development of effective reporting systems for primary care. METHODS: We conducted six full-day deliberative dialogue sessions with a purposive sample of 56 citizen-patients across three Canadian provinces (British Columbia, Ontario and Nova Scotia). Participants identified how they would use publicly reported performance data. We conducted a thematic analysis of the data by region. RESULTS: Common uses for primary care performance information emerged across all sessions. Participants most often discussed the utility of this information for community advocacy and participation in health system decision making. Similar barriers for using performance information to choose a primary care provider were identified in each region including the perceived lack of choice of providers and the high value placed on relationships with current providers. Finally, the value of public performance reporting in enhancing trust that people would receive good care was also a common theme. CONCLUSIONS: Citizen-patient perspectives highlight that public reporting on primary care performance could promote the health system's responsiveness by enabling public engagement in decision making at the community level. The role of public reporting in promoting trust rather than empowering patient choice may reflect unique elements of the Canadian health system's context.
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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 | 0.023 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.028 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.009 |
| 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".