Accelerating progress towards improved mental health and healthy behaviours in adolescents living in adversity: findings from a longitudinal study in South Africa
Bibliographic record
Abstract
Adolescents exposed to high levels of adversity are vulnerable to developing mental health challenges, with long-lasting adverse consequences. Promoting the psychological well-being of adolescents and protecting them from adverse experiences is crucial for their quality of life. There is a need for evidence on which combinations of protective factors can improve the wellbeing of adolescents to inform future programming efforts. We used data from a longitudinal study that took place in Khayelitsha, South Africa, a semi-urban impoverished community in Cape Town. Data were collected from adolescents when they were 12–14 years of age (n = 333) and again at follow-up when they were aged 16–19 years (n = 314). A path analysis was used to estimate associations between access to service, food security, safe environment, family support, and social support and five outcomes related to adolescent mental health and risky behaviours. The fitted model was used to calculate adjusted mean differences comparing different combinations of risk factors. Two protective factors (food security and safe environment) were positively associated with three outcomes relating to mental health and the absence of risky behaviours. Further investigation revealed that the presence of high food security and safer environments was associated with higher adjusted mean scores: +16.2% (p < .0001) in no substance use; +16.5% (p < .0001) in no internalising behaviour, +19.5% (p < .0001) in self-esteem; +12.2% (p < .0001) in positive peer relationships; and +11.4% (p < .0001) in no suicidal ideation. Interventions targeting adolescents, that aim to improve food security together with improving the safety of their environment, are likely to impact their well-being.
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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 | 0.002 | 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) | 0.000 | 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 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".