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Record W4391304437 · doi:10.3138/jmvfh-2023-0021

Increasing understanding of the barriers to military sexual trauma-related reporting and treatment seeking in Canada

2024· article· en· W4391304437 on OpenAlexafffundvenueabout
Andrea Brown, Heather Millman, Linna Tam‐Seto, Bibi Imre‐Millei, Ashley Ibbotson, Lori Buchart, Alexandra Heber, Marguerite Samplonius, Ashlee Mulligan, MaryAnn Notarianni, Margaret C. McKinnon

Bibliographic record

VenueJournal of Military Veteran and Family Health · 2024
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsInuit Tapiriit KanatamiVeterans Affairs CanadaUniversity of TorontoMcMaster University
FundersCanadian Institutes of Health ResearchCanadian Institute for Military and Veteran Health ResearchPublic Health AgencyPublic Health Agency of CanadaMcMaster University
KeywordsMental healthFeelingPsychiatryAnxietyPsychologyDepression (economics)Help-seekingPosttraumatic stressPsychotherapistMedicineClinical psychologySocial psychology

Abstract

fetched live from OpenAlex

LAY SUMMARY Military sexual trauma (MST) can cause many mental health problems, such as posttraumatic stress disorder, depression, and anxiety. Yet many people who experienced MST do not report what happened to them, do not seek mental health treatment, or drop out of treatment. Through experiences in an MST-specific community of practice, the authors heard many reasons why people do not report or do not seek treatment, including 1) feeling betrayed by the Canadian Armed Forces (CAF) and other military members, 2) not understanding what treatments are available, and 3) not knowing exactly what MST means. Knowing why people do not report MST or seek mental health treatment can help the CAF and treatment providers make changes to support people who experienced MST.

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)0.002
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.477

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0100.002
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0080.000

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 head0.116
GPT teacher head0.375
Teacher spread0.260 · 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 designQualitative
Domainnot available
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

Citations4
Published2024
Admission routes4
Has abstractyes

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