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Researcher-patient partnership generated actionable recommendations, using quantitative evaluation and deliberative dialogue, to improve meaningful engagement

2023· article· en· W4376279940 on OpenAlexafffund
Ellen Wang, Thalia Otamendi, Linda Li, Alison M. Hoens, Linda Wilhelm, Vikram Bubber, Elliot PausJenssen, Annette McKinnon, Shanon McQuitty, Kelly English, Aline S. Silva, Jenny Leese, Wasifa Zarin, Andrea C. Tricco, Clayon B. Hamilton

Bibliographic record

VenueJournal of Clinical Epidemiology · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsBC Mental Health & Substance Use ServicesProvincial Health Services AuthoritySaskatchewan Research Council (Canada)Queen's UniversitySt. Michael's HospitalSimon Fraser UniversityUniversity of OttawaUniversity of TorontoCanadian Arthritis Patient AllianceArthritis Research Centre of CanadaResearch CanadaCanadian Patient Safety InstitutePublic Health OntarioUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsAllianceGeneral partnershipScale (ratio)Public engagementMedical educationPsychologyPatient participationMedicineMEDLINEPublic relationsPolitical science

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.258
metaresearch head score (Gemma)0.373
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.742
Threshold uncertainty score0.915

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2580.373
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.002
Science and technology studies0.0070.005
Scholarly communication0.0110.010
Open science0.0040.018
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0100.002

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 head0.911
GPT teacher head0.704
Teacher spread0.208 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designQualitative
DomainMethods
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

Citations17
Published2023
Admission routes2
Has abstractno

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