Training and capacity development in patient-oriented research: Ontario SPOR SUPPORT Unit (OSSU) initiatives
Bibliographic record
Abstract
BACKGROUND: In Canada, the Canadian Institutes of Health Research launched the Strategy for Patient-Oriented Research (SPOR) in 2011. The strategy defines 'patient-oriented research' as a continuum of research that engages patients as partners, focuses on patient priorities, and leads to improved patient outcomes. The overarching term 'patient' is inclusive of individuals with personal experience of a health issue as well as informal caregivers including family and friends. The vision for the strategy is improved patient experiences and outcomes through the integration of patient-oriented research findings into practice, policy, and health system improvement. Building capacity in patient-oriented research among all relevant stakeholders, namely patients, practitioners, organizational leaders, policymakers, researchers, and research funders is a core element of the strategy. MAIN BODY: The objective of this paper is to describe capacity building initiatives in patient-oriented research led by the Ontario SPOR SUPPORT Unit in Ontario, Canada over the period 2014-2020. CONCLUSION: The Ontario SPOR SUPPORT Unit Working Group in Training and Capacity Development has led numerous capacity building initiatives: developed a Capacity Building Compendium (accessed greater than 45,000 times); hosted Masterclasses that have trained hundreds of stakeholders (patients, practitioners, organizational leaders, policymakers, researchers, and trainees) in the conduct and use of patient-oriented research; funded the development of online curricula on patient-oriented research that have reached thousands of stakeholders; developed a patient engagement resource center that has been accessed by tens of thousands of stakeholders; identified core competencies for research teams and research environments to ensure authentic and meaningful patient partnerships in health research; and shared these resources and learnings with stakeholders across Canada, North America, and internationally.
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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 | 0.056 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.004 | 0.005 |
| 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; 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".