Working with patients to co-produce visualizations comparing local and regional rates of attachment to primary care
Bibliographic record
Abstract
Context: Having a primary care provider is associated with better care experiences and lower costs of care. Administrative billing data that make up the Primary Care Data Reports [PCDR] can provide deeper understanding of the population in each of Ontario’s 57 Health Teams [OHTs], including how attributed patients engage with primary care. OHTs aim to organize health care to ensure integration and coordination across care settings. Objective: We engaged primary care patients/caregivers, providers, OHT representatives, and a graphic designer in participatory design sessions to create lay-friendly visualizations of local/provincial primary care data. Study Design and Analysis: Participatory design is characterized by three stages: initial exploration of work; discovery processes; and prototyping. A series of meetings with patient/caregiver advisors, OHT representatives, trainees and researchers served to provide orientation to the PCDR, share early findings about primary care locally and provincially, facilitated group discussions regarding principles of infographic design, and iteratively refine prototypes. Meeting minutes were shared with attendees after sessions to ensure an accurate reflection of the conversation; email exchanges resolved discrepancies and captured additional input. Patient partners were provided with honorariums to acknowledge their lived expertise. Setting: Community. Population Studied: Primary care patients. Outcomes: Rates of attachment to primary care, age, sex, visible minority, low income, housing instability, new to Ontario, and mental health diagnosis. Results: Up to 14 patient partners, three researchers, three OHT staff and two primary care trainees were involved in each of four 90-minute participatory design sessions conducted virtually. Discussion during sessions included perspectives on the strengths and limitations of administrative data in describing patients’ ideal involvement with primary care and contrasted that with the reality of their primary care experiences. Conclusions: Patient engagement in research is becoming widespread, but co-developing knowledge products with patient and health system partners is less common. Co-developed infographics can build community trust when members see their work contributing to data-driven discussions about primary health care in their region or province.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".