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Record W3174337177 · doi:10.1177/23333936211021576

The Mental Health Effects of Cumulative Lifetime Violence in Men: Disruptions in the Capacity to Connect with Others and Finding Ways to Reengage

2021· article· en· W3174337177 on OpenAlexafffundabout
Petrea Taylor, Susanne O’Donnell, Judith Wuest, Kelly Scott‐Storey, Charlene D. Vincent, J. Renee Malcom

Bibliographic record

VenueGlobal Qualitative Nursing Research · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversité de MonctonUniversity of New Brunswick
FundersCanadian Institutes of Health ResearchFondation de la recherche en santé du Nouveau-Brunswick
KeywordsMental healthAnxietyPsychologyDepression (economics)PerceptionClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

We report qualitative findings of our Men’s Violence Gender and Health Study, a multiple method study using a sequential design in which we explored the mental health manifestations of cumulative lifetime violence in men. Survey results revealed that higher cumulative lifetime violence scores were significantly associated with higher scores on depression, post-traumatic stress disorder, and anxiety in a community sample of men ( n = 685) living in Eastern Canada. To obtain a deeper understanding of men’s scores, we used an interpretive description approach to analyze data derived from 32 participant interviews. The main mental health manifestation of cumulative lifetime violence is perceptual interference, a sense of being disconnected or detached from others. This is managed by rectifying detachment, a process that includes efforts to gain connections with others. Findings suggest mental health needs in men with cumulative lifetime violence contradict gender role expectations to be stoic. Implications for nurses are explored.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.139
GPT teacher head0.523
Teacher spread0.384 · 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 teacher head, 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

Citations11
Published2021
Admission routes3
Has abstractyes

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