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Record W4409150417 · doi:10.70085/jtse.v4i1.159

Sexual Violence Characteristics and Postsecondary Women’s Mental Health

2025· article· en· W4409150417 on OpenAlexaffabout
Lana Stermac, Jenna Cripps, Touraj Amiri, Rebecca West

Bibliographic record

VenueJournal of Trauma Studies in Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMental healthPsychologySexual violenceClinical psychologyPsychiatryCriminology

Abstract

fetched live from OpenAlex

This study examined the mental health of postsecondary students who reported sexual violence and explored if mental health effects of depression, anxiety, stress as well as posttraumatic stress were related to the types and forms of sexual violence experienced. A sample of 924 culturally diverse female-identifying students in Ontario, Canada answered questionnaires about sexual violence experiences, mental health, and educational outcomes. The results of the study revealed that students reporting any form of sexual violence had higher scores for depression, anxiety, stress, and posttraumatic stress than students not reporting sexual violence. This study also found that the type of sexual violence experienced and the methods or tactics used were differentially associated with mental health symptoms. The results of this study highlight the importance of acknowledging the unique roles that types of sexual violence – particularly non-physical methods of coercion – have on the mental health of female postsecondary students.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.033
GPT teacher head0.399
Teacher spread0.366 · 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 designObservational
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

Citations0
Published2025
Admission routes2
Has abstractyes

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