Predictors of COVID-related changes in mental health in a South African sample of adolescents and young adults
Bibliographic record
Abstract
= 233 young people (mean age: 17.8 years at baseline, 55.6% female) living in a deprived neighbourhood near Cape Town, South Africa. Symptoms of depression (PHQ-9), anxiety (GAD-7) and alcohol use (AUDIT) were assessed during two waves of data collection, pre-pandemic (2018/19) and via phone interviews in June to October 2020, during South Africa's first COVID wave and subsequent case decline. Latent change score models were used to investigate predictors of changes in mental health. Controlling for baseline levels, we found increases in depression and anxiety but not alcohol use symptoms during the COVID-19 pandemic. Higher baseline symptoms were associated with smaller increases on all measures. Socio-economic deprivation (lack of household income, food insecurity) before and during COVID were associated with higher anxiety and depression symptom increases. Having had more positive experiences during COVID was associated with lower post-COVID onset anxiety and depression increases, and marginally with less alcohol use, while negative experiences (household arguments, worries) were linked to stronger symptom increases. Overall, in a sample of young people from an adverse environment in South Africa, we found increased mental health difficulties during the COVID-19 pandemic, though higher baseline symptoms did not necessarily predict stronger increases. Several factors pre- and post-COVID onset were identified that could be relevant for determining risk and resilience. In the long term, it will be key to address these structural drivers of well-being and to ensure mental health needs of young people are being met to support SSA countries in building back successfully from COVID-19 and preparing for future shock events.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".