High cellphone use associated with greater risk of depression among young women aged 15–24 years in Soweto and Durban, South Africa
Bibliographic record
Abstract
Background The ubiquity of cellular phone (cellphone) use in young people’s daily lives has emerged as a priority area of concern for youth mental health.Objective This study measured the prevalence of depression and its association with high cellphone use among youth in Soweto and Durban, South Africa.Methods We analysed cross-sectional, baseline survey data among youth aged 16–24 who participated in a dual-site cohort study, ‘AYAZAZI’, conducted from 2014 to 2017. The primary outcome was depression using the 10-item Center for Epidemiologic Studies Depression Scale, with a score of ≥ 10 indicating probable depression. Cellphone use was measured via self-reported average number of hours of active use, with ‘high cellphone use’ defined as daily usage of ≥ 8. Multivariable logistic regression models assessed the independent relationship between high cellphone use and probable depression, adjusting for potential confounders.Results Of 425 participants with a median age of 19 years (IQR = 18–21), 59.5% were young women. Overall, 43.3% had probable depression, with a higher prevalence among women (49.0% vs. 34.9%, P = .004). Nearly all (94.6%) owned a cellphone. About one-third (29.5%) reported spending ≥ 8 hours per day using their cellphone (39.3% of women vs. 14.9% of men, P < .001). In the overall adjusted model, youth reporting high daily cellphone use had higher odds of probable depression (aOR: 1.83, 95% CI: 1.16–2.90). In gender-stratified models, high daily cellphone use was associated with probable depression among women (aOR: 2.51, 95% CI: 1.47–4.31), but not among men (aOR: 0.87, 95% CI: 0.35–2.16).Conclusions Among a cohort of South African youth, we found a high prevalence of probable depression and high cellphone use (30%). The findings indicate a need for intersectoral initiatives focused on meaningful mental health support for South African youth to support positive growth and development.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".