Evaluation of the partners in research course: a patient and researcher co-created course to build capacity in patient-oriented research
Bibliographic record
Abstract
Abstract Background In the past decade, patient-oriented research (POR) has been at the forefront of healthcare research in Canada because it has the potential to make research more meaningful and relevant to patient needs. Despite this growing emphasis on and expectation to conduct POR, there is limited guidance about how to apply POR in practice. To address this capacity building need, the Knowledge Translation (KT) Program and patient partners co-designed, delivered, and evaluated Partners in Research (PiR), a 2-month online course for patients and researchers to collectively learn how to conduct and engage in POR. Methods PiR was delivered to 4 cohorts of patients and researchers between 2017 and 2018. For each cohort, we evaluated the impact of the course on participants’ knowledge, self-efficacy, intentions, and use of POR using surveys at 3 time points: baseline, post-course and 6-months post-course. We also monitored the process of course design and delivery by assessing implementation quality of the PiR course. Participants were asked to rate their satisfaction with course format, course materials, quality of delivery and their level of engagement via a 7-point Likert scale in the post-course survey. Results A total of 151 participants enrolled in the PiR course throughout the 4 cohorts. Of these, 49 patients and 33 researchers (n = 82 participants) consented to participate in the course evaluation. Process and outcome evaluations collected over a 9-month period indicated that participation in the PiR course increased knowledge of POR concepts for patients (p
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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 | 0.049 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".