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Record W3138478815 · doi:10.1186/s40900-021-00258-1

Patient-partner engagement at the Centre de recherche du CHUS in the Province of Québec, Canada: from an intuitive methodology to outreach after three years of implementation

2021· article· en· W3138478815 on OpenAlexaffabout
Denis Boutin, Susan C. Mastine, Luc Beaubien, Maryse Berthiaume, Denise Boilard, Jaime Borja, Édouard Botton, Janie Boulianne-Gref, Sylvie Breton, Christian‐Alexandre Castellano, Gisèle Charpentier, F. Counil, Marie-Josée Cozmano, Pierre Dagenais, Guy Drouin, Marie-Josée Fortier, Caroline Francoeur, Louise Gagné, David Héraud, Denise Hêtu, Marie-Pier Houde, Ginette Ladouceur, Marjolaine Landry, Élisabeth Leblanc, Christine Loignon, Valéry Lussier, Annie Morin, Nathalie Ouellet, Claude Quintin, Avinash Ramnarine, Catherine Wilhelmy, Amy Svotelis, Marie-Ève Thibault, William D. Fraser, Marie‐Claude Battista

Bibliographic record

VenueResearch Involvement and Engagement · 2021
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanUniversité du Québec à Trois-RivièresUniversité de SherbrookeCentre Hospitalier Universitaire de Sherbrooke
Fundersnot available
KeywordsOutreachLibrary scienceCorporate governanceCommunity engagementPolitical sciencePublic relationsMedical educationMedicineManagement

Abstract

fetched live from OpenAlex

BACKGROUND: Medical societies and funding agencies strongly recommend that patients be included as partners in research publications and grant applications. Although this "top-down" approach is certainly efficient at forcing this new and desirable type of collaboration, our past experience demonstrated that it often results in an ambiguous relationship as not yet well integrated into the cultures of either patients' or the researchers'. The question our group raised from this observation was: "How to generate a cultural shift toward a fruitful and long-lasting collaboration between patients and researchers? A "bottom-up" approach was key to our stakeholders. The overall objective was to build a trusting and bidirectional-ecosystem between patients and researchers. The specific objectives were to document: 1) the steps that led to the development of the first patient-partner strategic committee within a research center in the Province of Québec; 2) the committee's achievements after 3 years. METHODS: Eighteen volunteer members, 12 patient-partners and 6 clinician/institutional representatives, were invited to represent the six research themes of the Centre de recherche du CHU de Sherbrooke (CRCHUS) (Quebec, Canada). Information on the services offered by Committee was disseminated internally and to external partners. Committee members satisfaction was evaluated. RESULTS: From May 2017 to April 2020, members attended 29 scheduled and 6 ad hoc meetings and contributed to activities requiring over 1000 h of volunteer time in 2018-2019 and 1907 h in the 2019-2020 period. The Committee's implication spanned governance, expertise, and knowledge transfer in research. Participation in these activities increased annually at local, provincial, national and international levels. The Patient-Partner Committee collaborated with various local (n = 7), provincial (n = 6) and national (n = 4) partners. Member satisfaction with the Committee's mandate and format was 100%. CONCLUSIONS: The CRCHUS co-constructed a Patient-Partner Strategic Committee which resulted in meaningful bilateral, trusting and fruitful collaborations between patients, researchers and partners. The "bottom-up" approach - envisioned and implemented by the Committee, where the expertise and the needs of patients complemented those of researchers, foundations, networks and decision-makers - is key to the success of a cultural shift. The CRCHUS Committee created a hub to develop the relevant intrinsic potential aimed at changing the socio-cultural environment of science.

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 imitation

Not 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.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.684
GPT teacher head0.557
Teacher spread0.127 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations5
Published2021
Admission routes2
Has abstractyes

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