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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.016 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".