Childhood Psychological Problems in School Settings in Rural Southern Africa
Bibliographic record
Abstract
BACKGROUND: Many children can be exposed to multiple adversities in low and middle-income countries (LMICs) placing them at potential risk of psychological problems. However, there is a paucity of research using large representative cohorts examining the psychological adjustment of children in school settings in these countries. Children's psychological adjustment has been shown to affect educational progress which is critical for their future. This study, based in a rural, socio-economically disadvantaged area of South Africa, aimed to examine the prevalence of children's psychological problems as well as possible risk and protective factors. METHODS: Rates of psychological problems in 10-12 year olds were examined using teacher- and child-report questionnaires. Data on children from 10 rural primary schools, selected by stratified random sampling, were linked to individual and household data from the Agincourt health and socio-demographic surveillance system collected from households over 15 years. RESULTS: A total of 1,025 children were assessed. Teachers identified high levels of behavioural and emotional problems (41%). Children reported lower, but substantial rates of anxiety/depression (14%), and significant post-traumatic stress symptoms (24%); almost a quarter felt unsafe in school. Risk factors included being a second-generation former refugee and being from a large household. Protective factors highlight the importance of maternal factors, such as being more educated and in a stable partnership. CONCLUSION: The high levels of psychological problems identified by teachers are a serious public health concern, as they are likely to impact negatively on children's education, particularly given the large class sizes and limited resources in rural LMIC settings. Despite the high levels of risk, a proportion of children were managing well and research to understand resilience could inform interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".