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Record W4406844557 · doi:10.1016/j.ekir.2024.11.578

WCN25-3209 PATIENT PARTNER RECRUITMENT: CO-DEVELOPING A PATIENT-ORIENTED RESEARCH PEER ENGAGEMENT AS A CAPACITY-BUILDING STRATEGY IN A NATIONAL KIDNEY HEALTH RESEARCH NETWORK

2025· article· en· W4406844557 on OpenAlexaff
Melanie D. Talson, Charles D. Cook, Kelly Loverock, Claudia Wilde

Bibliographic record

VenueKidney International Reports · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsEmmanuel Bible CollegeCanadian Association of Nurses in Oncology
Fundersnot available
KeywordsMedicineCapacity buildingEconomic growth

Abstract

fetched live from OpenAlex

Patient-oriented research (POR) is increasingly promoted in kidney health research and is regarded as integral to ensuring that research aligns with patient priorities. Involving patients and caregivers as partners in research (patient partners) engaged in every stage of the research, from identifying research questions to co-developing protocols, analyzing data, and knowledge dissemination activities,supportsa relevant research program as well as fostering an empowering experience for patients and caregivers.SupportingPOR and acknowledging the depth and breadth of knowledge that patient partners contribute from their lived experience is integral to ensuring research is most relevant for those for whom it matters most.

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)0.075
metaresearch head score (Gemma)0.094
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.925
Threshold uncertainty score0.394

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0750.094
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0080.002
Scholarly communication0.0070.005
Open science0.0030.019
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0820.024

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 head0.629
GPT teacher head0.592
Teacher spread0.037 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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