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Record W4405224886 · doi:10.1093/jsxmed/qdae161.074

(092) DESCRIPTIVE SOCIODEMOGRAPHIC STUDY AND VARIABLES RELATED TO SUICIDE IN A SAMPLE OF VICTIMS OF SEXUAL VIOLENCE SEEKING TREATMENT AT A PSYCHIATRIC OUTPATIENT CLINIC IN SãO PAULO, BRAZIL

2024· article· en· W4405224886 on OpenAlexaff
Fernanda Garcia Varga de Sobral, Marco de Tubino Scanavino, Patrícia do Espírito Santo Gonçalves, R. M. Anjos

Bibliographic record

VenueThe Journal of Sexual Medicine · 2024
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsSt Joseph's Health CareLondon Health Sciences CentreLawson Health Research InstituteWestern University
Fundersnot available
KeywordsPsychiatrySexual violenceOutpatient clinicDescriptive researchMedicineSample (material)PsychologyClinical psychologyDescriptive statisticsNursingSociology

Abstract

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Abstract Introduction Sexual violence, a complex and often underreported phenomenon in victim assistance services, carries the severe risk of suicide, directly impacting public health management and underscoring the necessity for prevalence studies to guide intervention and prevention efforts. Objective This study aimed to characterize the sociodemographic factors and suicide risk among victims of sexual violence. Methods Cross-sectional, observational, and analytical study with a sample of 54 participants. Of these, 5 (9.26%) were male and 49 (90.74%) were female. Participants underwent a sociodemographic interview, application of the Columbia-Suicide Severity Rating Scale (C-SSRS Lifetime/Recent version), and a standardized diagnostic interview Mini International Neuropsychiatric Interview. Data were collected between 2021 and 2024. People aged 14 and older with satisfactory cognitive capacity to understand the instruments were included, while participants with acute psychotic symptoms presenting delusional ideation, hallucinations that could impair and hinder the application of the research instruments were excluded. Results In the descriptive analysis conducted using STATA, sociodemographic data showed that the average age among women was 32 years (SD 10.14) and among men was 28 years (SD 8.23) (p = 0.35). Education level in elementary school, high school and college was, respectively, 55.10%, 22.45% and 22.45% for women, and 40%, 60%, and 0%) for men (p = 0.23). Regardind marital status, it was married or common law, single, and separated or divorced, respectively, 34.04%, 55.32%, and 10.64% for women; and 0%, 59.62%, and 9.62% for men (p = 0.25). Regardingsuicidal behavior, 65.22% of women reported suicidal ideation with planning during their lifetime, while 50% of men reported it (p = 0.61). In terms of suicide attempts, 59.18% of women had at least one suicide attempt during their lifetime, compared to 40% of men (p = 0.64). Regarding suicidal risk, it was present in 73.47% for women and 60% for men (p = 0.61). Regarding multivariate logistic regression, lower levels of education were statistically significantly associated with suicide risk (p-value 0.007), while gender, and marital status did not show statistically significant associations. Conclusions The study conducted a brief screening of sociodemographic data and suicide risk among men and women sexual violence victims, seeking help for mental health difficulties. The results show no significant differences between men and women according sociodemographic and suicide variables. Those with lower level of education showed higher risks for suicide. Further studies with more representative male sample or with a control group will help to increase evidences. Those data suggests a greater vulnerability to sexual violence victims with low schooling for suicide risk, meaning it is a group that needs different interventions. Psychoeducation potentially can have a special role for them. Disclosure No.

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 score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.098
GPT teacher head0.445
Teacher spread0.348 · 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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