Tracing impact: building capacity in patient-oriented primary care research in Ontario and beyond
Bibliographic record
Abstract
Context: Capacity development is a key component of the Canadian Strategy for Patient-Oriented Research (SPOR) SPOR has articulated a framework for capacity development in patient-oriented research (POR) that includes five guiding principles (ensuring capacity for meaningful patient engagement, supporting careers, collaborating, mobilizing existing expertise, and building capacity to apply research evidence). Patient Expertise in Research Collaboration (PERC) – primary health care (PHC) is a centre supported by the Ontario SPOR Support Unit. Together with ten patients who have experience managing chronic illness or life-limiting conditions, PERC encourages and supports the meaningful engagement of patients as partners in PC research. In Ontario, Canada, more people access PC than any other type of healthcare. If health systems strive to improve patient experience, and PC visits comprise most of the health care delivered in those systems, then the research informing this sector should be oriented to the needs of PC patients. Objective: This study describes PERC’s activities and how PERC impacts capacity building in patient-oriented PC research. Study Design and Analysis: Process evaluation Setting: Community. Population Studied: PC researchers, PERC patient partners. Instrument: Document analysis, website metrics. Outcome Measures: SPOR’s capacity development framework. Results: To build capacity for meaningful engagement, PERC supported>30 PC research/health system representatives from multiple institutions across four Canadian provinces. PERC’s support includes providing strategic advice and input into grant development, reviewing study documents associated with patient engagement plans and methodology, writing letters of support, and advising on patient engagement strategies and resources. PERC supports careers by contributing to a transdisciplinary PC research training program and providing three annual fellowships. Fellows contribute to PERC activities and receive strategic advice from patients about integrating POR into their projects. PERC’s patient partners became increasingly embedded in the provincial network and embraced opportunities to mobilize their expertise and meaningfully advance patient partnership in PC research. Website analytics indicate the PC community’s uptake of PERC resources. Conclusions: PERC’s activities build capacity in patient-oriented PC research among researchers, patient partners, and trainees in Ontario and beyond.
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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 | 0.159 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.022 | 0.028 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.005 | 0.029 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".