MétaCan
Menu
Back to cohort
Record W4400452865 · doi:10.1136/bmjebm-2024-sdc.196

197 Passerelle: a national hub supporting and promoting capacity development for patient-oriented research

2024· article· en· W4400452865 on OpenAlexaffabout
Yvonne Pelling, Emilie Grenier, Gaelle Bourriquen, Marie-Dominique Poirier, Emilie Goupil-Nantel, Myriam Rodrigue, Malcolm King, Dan Goldowitz, Marie-Ève Poitras, Nicolas Fernandes, Rachael Bosma, Samuel Turcotte, Annie Le Blanc

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsBC Children's HospitalUniversité de MontréalUniversité de SherbrookeUniversity of Saskatchewan
Fundersnot available
KeywordsComputer scienceProcess managementKnowledge managementSystems engineeringEngineering managementBusinessEngineering

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.025
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.975
Threshold uncertainty score0.874

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0150.009
Scholarly communication0.0140.006
Open science0.0050.019
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0440.014

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.

Opus teacher head0.120
GPT teacher head0.419
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainMethods
GenreOther

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

Same topicBiomedical Ethics and RegulationFrench-language works237,207