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Record W4377224085 · doi:10.31478/202305b

Identifying Credible Sources of Health Information in Social Media: Phase 2—Considerations for Non-Accredited Nonprofit Organizations, For-Profit Entities, and Individual Sources

2023· review· en· W4377224085 on OpenAlexaff
Helen Burstin, Susan J. Curry, Megan L. Ranney, Vineet M. Arora, Brian Boxer Wachler, Wen‐Ying Sylvia Chou, Ricardo Correa, Donna R. Cryer, Don S. Dizon, Efrén J. Flores, Gerald Harmon, Anjali Jain, Kevin Johnson, Christine Lainé, Lindsey Leininger, Graham T. McMahon, Laura A. Michaelis, Ripudaman Minhas, Richard A. Mularski, John Oldham, Rema Padman, Claude Pinnock, Jessica Rivera, Brian G. Southwell, Antonia M. Villarruel, Katrine L. Wallace

Bibliographic record

VenueNAM Perspectives · 2023
Typereview
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersUniversity of North TexasWorld Health Organization
KeywordsAccreditationSocial mediaNot for profitBusinessPublic relationsNon profitHealth informationPhase (matter)MarketingHealth carePolitical scienceBusiness administration

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.220
metaresearch head score (Gemma)0.238
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.220
Threshold uncertainty score0.962

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2200.238
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0080.006
Science and technology studies0.0030.004
Scholarly communication0.0130.022
Open science0.0050.010
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0130.002

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 head0.273
GPT teacher head0.479
Teacher spread0.206 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations10
Published2023
Admission routes1
Has abstractyes

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