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Record W4389938368 · doi:10.1016/s2213-8587(23)00347-9

Bringing an end to diabetes stigma and discrimination: an international consensus statement on evidence and recommendations

2023· review· en· W4389938368 on OpenAlexaff
Jane Speight, Elizabeth Holmes‐Truscott, Matthew Garza, Renza Scibilia, Sabina Wagner, Asuka Kato, Víctor Pedrero, Sonya S. Deschênes, Susan Guzman, Kevin L. Joiner, Shengxin Liu, Ingrid Willaing, Katie M. Babbott, Bryan Cleal, Jane K. Dickinson, Jennifer A. Halliday, Eimear Morrissey, Giesje Nefs, Shane O’Donnell, Anna Serlachius, Per Winterdijk, Hamzah Alzubaidi, Bustanul Arifin, Liz Cambron-Kopco, Corinna Santa Ana, Emma Davidsen, Mary de Groot, Maartje de Wit, Phyllisa Smith Deroze, Stephanie Haack, Richard I. G. Holt, Walther Jensen, Kamlesh Khunti, Karoline Kragelund Nielsen, Tejal Lathia, Christopher J Lee, Bridget McNulty, Diana Naranjo, Rebecca L. Pearl, Suman Prinjha, Rebecca M. Puhl, Anita Sabidi, Chitra Selvan, Jazz Sethi, Mohamed K. Seyam, Jackie Sturt, Mythily Subramaniam, Helle Terkildsen Maindal, Virginia Valentine, Michael Vallis, Timothy Skinner

Bibliographic record

VenueThe Lancet Diabetes & Endocrinology · 2023
Typereview
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsDalhousie University
FundersDiabetes VictoriaNovo Nordisk FondenDeakin UniversitySteno Diabetes Center Copenhagen
KeywordsMedicineStigma (botany)Prejudice (legal term)Social stigmaBlamePsychiatrySocial psychologyFamily medicinePsychology

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)0.066
metaresearch head score (Gemma)0.097
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.066
Threshold uncertainty score0.348

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0660.097
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0130.008
Bibliometrics0.0080.007
Science and technology studies0.0020.005
Scholarly communication0.0110.009
Open science0.0060.007
Research integrity0.0140.017
Insufficient payload (model declined to judge)0.0070.002

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 head0.361
GPT teacher head0.546
Teacher spread0.186 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations119
Published2023
Admission routes1
Has abstractno

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