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Record W4410971259 · doi:10.21203/rs.3.rs-6727717/v1

Recognition, remuneration and reimbursement of patient and public involvement partners in pragmatic randomised controlled trials. A survey of author practices

2025· preprint· en· W4410971259 on OpenAlexafffund
Stuart G. Nicholls, Pascale Nevins, Grace Fox, Shelley Vanderhout, Jamie Brehaut, Kelly Carroll, Dean Fergusson, Alicia Hilderley, Colin Macarthur, Beth K. Potter, Maureen Smith, Anne Spinewine, Charles Weijer, Monica Taljaard

Bibliographic record

VenueResearch Square · 2025
Typepreprint
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsWestern UniversitySickKids FoundationUniversity of OttawaHospital for Sick ChildrenTrillium Health CentreOttawa Hospital
FundersSchool of Medicine, University of North Carolina at Chapel HillCanadian Institutes of Health ResearchUniversité François-RabelaisBrown UniversityUniversity of AberdeenEli Lilly and Company
KeywordsRemunerationReimbursementPublic involvementFamily medicinePhysical therapyMedicinePsychologyBusinessPublic relationsMedical educationPolitical scienceFinanceLaw

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)0.288
metaresearch head score (Gemma)0.630
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score0.878

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2880.630
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.003
Science and technology studies0.0010.002
Scholarly communication0.0040.006
Open science0.0020.003
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.612
GPT teacher head0.589
Teacher spread0.024 · 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 designObservational
DomainIncentives
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

Citations0
Published2025
Admission routes2
Has abstractno

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