MétaCan
Menu
Back to cohort
Record W4385349209 · doi:10.1017/cts.2023.600

Integrating community voices in the research continuum: Perspectives on a consultation service

2023· article· en· W4385349209 on OpenAlexaff
Crystal Evans, Joy P. Nanda, Pamela Ouyang, Lee Bone, Samuel Byiringiro, Cyd Lacanienta, Roger Clark, Christine Weston, Hae‐Ra Han, Mia Terkowitz, Barbara Bates-Hopkins, Panagis Galiatsatos, Ashley Xu, Sarah Stevens, Cheryl Dennison Himmelfarb

Bibliographic record

VenueJournal of Clinical and Translational Science · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsCommunity Sector Council Newfoundland and Labrador
FundersNational Institutes of HealthUniversity of CambridgeNational Center for Advancing Translational SciencesJohns Hopkins University
KeywordsStakeholderPublic relationsService (business)Coronavirus disease 2019 (COVID-19)Medical educationPolitical scienceNursingPsychologyMedicineLibrary scienceBusiness

Abstract

fetched live from OpenAlex

The Community Research Advisory Council (C-RAC) of the Johns Hopkins Institute for Clinical and Translational Research was established in 2009 to provide community-engaged research consultation services. In 2016-2017, C-RAC members and researchers were surveyed on their consultation experiences. Survey results and a 2019 stakeholder meeting proceeding helped redesign the consultation services. Transitioning to virtual consultations during COVID-19, the redesigning involved increasing visibility, providing consultation materials in advance, expanding member training, and effective communications. An increase in consultations from 28 (2009-2017) to 114 (2020-2022) was observed. Implementing stakeholder-researcher inputs is critical to holistic and sustained community-engaged research.

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)0.115
metaresearch head score (Gemma)0.125
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.885
Threshold uncertainty score0.609

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1150.125
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.004
Science and technology studies0.0730.042
Scholarly communication0.0390.023
Open science0.0070.052
Research integrity0.0210.033
Insufficient payload (model declined to judge)0.0120.002

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 head0.745
GPT teacher head0.656
Teacher spread0.089 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical and Translational ScienceSame topicMental Health and Patient InvolvementFrench-language works237,207