Results from the OurCare Survey: Public Perspectives on Primary Care in Canada
Bibliographic record
Abstract
Context: Canada’s primary care system is in crisis and in urgent need of reform. Reforms should be informed by patients, caregivers and the public but little research has been done to understand their perspectives. Objective: We conducted a national survey to understand people’s experiences with primary care and their values, needs, and preferences. The survey is the first phase of OurCare, an initiative to engage the public on the future of primary care in Canada. Study Design and Analysis: Our anonymous, bilingual survey was distributed across Canada between September and October 2022 in partnership with Vox Pop Labs, an organization that aims to enhance democratic participation. Vox Pop Labs sent a unique link to 63,552 people on their proprietary panel, following up with two personalized reminders. An open survey link was also promoted through traditional and social media. Only completed questionnaires were analyzed. Survey responses from the two links were combined and weighted via iterative proportional fitting (raking) according to estimates from the 2021 Statistics Canada Census to ensure respondents roughly reflected the demographics of Canada. Setting or Dataset: Canada. Population Studied: Adults aged 18 years and over residing in Canada. Outcome Measures: Experience with primary care; views on most important attributes of care; experiences and views on walk-in clinics, virtual care, team-based care, access to their own medical data; and willingness to re-imagine primary care. Results: We received 9279 completed surveys. Overall, 22% of respondents reported not having a family doctor or nurse practitioner (NP) but there were large regional differences (Ontario 13%, Quebec 31%, Atlantic region 31%). The attribute of primary care ranked as most important was that their primary care provider “know me as a person and consider all the factors that affect my health”. 90% of people felt comfortable or very comfortable getting support from another member of the team if their family doctor or NP recommended it. 91% were willing to see the same NP consistently for most things and 76% were willing to see any family doctor or NP in the practice if they had access to their records. Conclusions: Almost one quarter of people living in Canada reported not having a family doctor or NP. Respondents were generally open to new ways of organizing primary care but there was stronger agreement for proposals that maintained relational continuity with a single clinician.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".