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Record W4400591785 · doi:10.52589/ajhnm-uyamxcif

Exploring Contextual and Individual Factors Influencing Prevalence of Sexual Assault among Female Young People in Anambra State, Nigeria

2024· article· en· W4400591785 on OpenAlexaff
T. S. Florence, Elisabeth Julia, N. O. Hope, A. G. Mariam, M. Salima

Bibliographic record

VenueAfrican Journal of Health Nursing and Midwifery · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFocus groupPsychologyQualitative researchSocioeconomic statusSexual abuseQualitative propertySuicide preventionPoison controlMedicineEnvironmental healthPopulationSociologySocial science

Abstract

fetched live from OpenAlex

Sexual assault poses a global threat, impacting victims, families, and societies both in short-term and long-term. This study aims to understand the contextual and individual factors contributing to the high prevalence of sexual assault among young females (aged 10 to 24) in Anambra State, Nigeria. Conducted as a descriptive qualitative study, data were gathered from thirty-one participants, including twenty-three stakeholders and eight rape victims, through two focus group discussions (FGDs) and thirteen in-depth interviews (IDIs). Recorded data was transcribed verbatim and analyzed thematically using NVivo 12. The study identified eight major contextual factors influencing sexual assault: socioeconomic status, drug abuse, level of morality, policy implementation, insecurity, and unemployment. Significant individual factors include the occupation of young people, parenting style, dressing choices, educational institution affiliation, individual conduct, and indiscriminate use of electronic devices. The study concludes that policies aimed at revitalizing moral instruction in schools, curbing drug use, addressing indecent dressing, and regulating phone use among young people are crucial in controlling sexual assault.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.034
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.104
GPT teacher head0.352
Teacher spread0.248 · 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 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
Published2024
Admission routes1
Has abstractyes

Explore more

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