197 Passerelle: a national hub supporting and promoting capacity development for patient-oriented research
Bibliographic record
Abstract
Introduction The Canadian Institutes of Health Research (CIHR)’s Strategy for Patient-Oriented Research (SPOR) National Training Entity, PASSERELLE, is a Canada-wide network of networks which builds on the strengths and successes of the SPOR environment’s foundational work, lessons learned, and established partnerships. It was founded to rally, leverage, and sustain what has been accomplished in a comprehensive hub to better serve the growing patient-oriented research (POR) community and support the capacity to innovate in health research. Overall Objective To support and promote the collective efforts to develop and sustain capacity in patient-oriented research across Canada and internationally, in a collaborative, inclusive, culturally safe, and sustainable environment. Functions and Activities PASSERELLE supports and engages all learners (e.g., patients, communities, health/social service professionals, decision-makers, academic trainees, and researchers) and organizations. Within its core functions, PASSERELLE serves as a central body for POR capacity strengthening, providing tailored learning pathways and curriculums through its centralized inventory of learning and training activities, its resource repository, and mentoring/networking opportunities. It further supports and empowers trainees by offering and supporting scholarships and fellowships in collaboration with SPOR Entities, funding agencies, and institutions. It engages with the POR community to facilitate the sharing of rapidly emerging science and best practices in POR through shared events dedicated to patient engagement and POR. Lastly, it provides guidance, support and orientation to teams and organizations interested in POR. Expected Outcomes Ultimately, PASSERELLE will contribute to creating a cadre of highly qualified researchers and knowledge users who represent the future of POR, advancing the science and practice of POR, and integrating POR principles, practices, and findings within health care contexts.
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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 | 0.025 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.044 | 0.014 |
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".