A Multi-Pronged Approach to Engaging Patients and Other Key Stakeholders in a Mixed-Methods Study Investigating Patients’ Exp
Bibliographic record
Abstract
Context: At onset of the COVID-19 pandemic, primary care quickly transitioned to the use of virtual care. Although much is known about the challenges and opportunities of virtual care for providers and practices, little remains known about the patient experience with virtual care in primary care. The involvement of patients and other key stakeholders as partners in the research process is crucial to generate relevant findings to guide policy and decision-makers in a way that enhances quality of care and patient outcomes. Objective: Describe a multi-pronged approach to inform a population based, mixed-methods research study investigating patients’ experiences with virtual care in Ontario, Canada. Study Design: Mixed-methods - a province-wide survey and descriptive qualitative interviews running in parallel. Our multi-pronged approach engaged Patients, Researchers, Policy makers and other data users throughout the study. I) Patients: We recruited a Patient Advisory Committee through patient advocacy groups and health-oriented organizations, and selected individuals to maximize geographical, gender, and sociocultural variation. II) Professional Organizations: We integrated the Association of Family Health Teams of Ontario, Alliance for Healthier Communities, and Ontario College of Family Physicians as key members of the research team. III) Primary Care Practices: We partnered with primary care practices who were data users. Practices disseminated the survey to their patients. IV) Policy and Decision-Makers: We conducted ongoing consultations with stakeholders from Ontario Health and Ontario Ministry of Health for a reciprocal dynamic that informed the research process and enabled rapid uptake of research findings by key stakeholders. Our multi-pronged process used a mixture of involvement and collaboration models of engagement. Results: 55 interviews were conducted with patients and 534 survey respondents met eligibility criteria. The multi-pronged approach provided success with rapid dissemination of results to inform immediate system and practice-level decisions on the use of virtual care. We produced a comprehensive report for provincial policy and decision-makers, conducted 3 presentations to provincial policy and decisionmakers, provided each participating practice with detailed practice-specific reports, and participated in an invited panel. Conclusions: Patient and stakeholder engagement is a key component to primary care research.
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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 | 0.219 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".