Hidden desires, echoed distress: Dissecting Nigeria’s sexting landscape and its ties to depression
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In a broader context of increasing incidences of sexting-related backlashes resulting in victims’ depression and, at times, suicide in Nigeria, this study examines the prevalence, trends, and mental health implications of sexting among 700 Nigerian social media users. With the help of the Patient Health Questionnaire-9 and the Sexting Behaviors and Motives Questionnaire, we found that 58% of respondents engaged in sexting, a high percentage given the cultural conservatism of Nigeria. In addition, more than 41% admitted forwarding or having another forward sexted images or messages without the victims’ consent, increasing the risk of cyberbullying and subsequent mental health problems. In our study, we found a strong positive relationship between sexting and depression; the effects of sexting on depression differed for men and women: Men sexters exhibited higher depression levels than women. Our analysis, which employed descriptive, regression, and Structural Equation Model (SEM) methodologies, suggests that despite regional cultural disparities, sexting behaviors are surprisingly uniform across Nigeria. This study underscores the urgent need for informed strategies addressing digital privacy, security, and mental well-being in the context of sexting in Nigeria. • The study finds that 58% of Nigerian social media users engage in sexting. • Over 41% admit to forwarding sexts non-consensually, highlighting privacy concerns. • There is a correlation between sexting behaviors and symptoms of depression. • The study shows notable gender differences in sexting and depression.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it