Prevalence of Sexual Harassment in a Faith-Based Institution of Higher Learning in South-Western Nigeria
Bibliographic record
Abstract
Tertiary institutions are ivory towers where academic and moral excellences are expected to be promoted. However, it has become where sexual harassment is a common deviant practice. This study aimed to determine the knowledge and prevalence of sexual harassment among college students in a private institution in Ogun state, Nigeria. The study employed a cross-sectional survey design to enroll 394 college students across the undergraduate levels of the university, using a 22-item self-administered validated instrument by a multi-stage sampling procedure. Demographic characteristics, knowledge and prevalence of college students on sexual harassments were measured. The mean age of respondents was 18.84 ± 1.833years, with majority (71%) being between ages 15–19, and 70% being females. About 39% and 38% respectively confirmed they have been victims of sexual harassment or know friends that have been harassed sexually. Level of knowledge on sexual harassment was 3.90 ± 0.866 and prevalence of sexual harassment was 4.88 ± 3.194 computed on a mean and standard deviation scale, translate to a prevalence score of 40.67%. Prevalence of Sexual Harassment was high among college student even though they had good Knowledge. A more proactive measures needs to be put in place to curb the menace in a Christian own institution of higher learning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".