Mental distress and substance use among rural Black South African youth who are not in employment, education or training (NEET)
Bibliographic record
Abstract
Background: South Africa (SA) has one of the highest rates of youth unemployment and youth who are not in employment, education or training (NEET), even higher among Black South Africans. SA’s NEET rates are 3 times those of UK; 5.4 times of Germany; 1.3 times of Brazil; and 2.5 times of Malaysia. Given that youths between 15 and 24 years of age make up 24% of the total population, these are significant challenges for the economy and further fuel the cyclical, pervasive and enduring nature of poverty. We hypothesised that rural youth who are NEET would have a greater prevalence of mental disorders and higher rates of substance use compared to their non-NEET counterparts. The objective of the study is to determine the differences in rates of psychological distress and substance use between NEET and non-NEET rural African 14- to 24-year-old young men. Methods: The study took place in a remote and rural district municipality in KwaZulu-Natal, South Africa. We divided the district’s five sub-municipalities into two clusters (large and small) and randomly selected one from each cluster for inclusion in the study. We further randomly selected wards from each sub-municipality and then rural settlements from each ward, for inclusion in the study. We recruited young men as part of a larger study to explore sociocultural factors important in gender-based violence in rural SA. We compared 15- to 19-year old and 20- to 24-year old youth NEET and non-NEET on rates of psychological distress symptoms (depression, anxiety, suicidal thoughts, hopelessness and worthlessness) and substance misuse (including alcohol, cannabis, other recreational drugs) using a Multivariate Analysis of Variance (MANOVA) statistics at p < .005 level of significance level. Results: About 23% of the 355 male participants were NEET. There were no statistically significant differences in psychological distress or substance use between youth NEET and non-NEET, controlling for age. Conclusion: The study highlights difficult transitions to post-secondary education and work for Black youth in rural SA where opportunities for employment are limited. Education, training and employment appear to offer limited benefit.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".