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Record W4388862571 · doi:10.24095/hpcdp.43.10/11.09

Glossary of terms: A shared understanding of the common terms used to describe psychological trauma, version 3.0

2023· article· en· W4388862571 on OpenAlexafffundvenue
Valerie Testa, Alexandra Heber, Dianne Groll, Kimberly Ritchie, Linna Tam‐Seto, Ashlee Mulligan, Emily Sullo, Amber Schick, Elizabeth Bose, Yasaman Jabbari, Jillian Lopes, R. Nicholas Carleton

Bibliographic record

VenueHealth Promotion and Chronic Disease Prevention in Canada · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMedical Research and Practices
Canadian institutionsUniversity of ReginaTrent UniversityMcMaster UniversityQueen's UniversityRoyal Ottawa Mental Health CentreVeterans Affairs CanadaUniversity of TorontoCanadian Institute for Public Safety Research and TreatmentPublic Health Agency of Canada
FundersMinistère de la Défense NationaleCanadian Institute for Military and Veteran Health ResearchUniversity of TorontoCanadian Armed ForcesU.S. Department of Veterans AffairsMental Health CommissionUniversity of AlbertaQueen's UniversityMount Saint Vincent UniversityUniversity of OttawaPublic Health Agency of CanadaMcMaster UniversityLawson Health Research InstituteTemerty Faculty of Medicine, University of TorontoTrent UniversityDivision of ChemistryUniversity of ReginaPublic Health Agency
KeywordsGlossaryHumanitiesSynonym (taxonomy)PhilosophyLinguisticsZoologyGenusBiology

Abstract

fetched live from OpenAlex

Terms in the current glossary are arranged alphabetically by the most commonly used synonym.Most of the terms have two complementary definitions: a "

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)0.004
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.976
Threshold uncertainty score0.336

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.022
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0120.017
Science and technology studies0.0030.002
Scholarly communication0.0070.008
Open science0.0030.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.1010.056

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 head0.304
GPT teacher head0.506
Teacher spread0.202 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations92
Published2023
Admission routes3
Has abstractyes

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