Education as a protective factor for mental health risks among youth living in highly dangerous regions in Afghanistan
Bibliographic record
Abstract
BACKGROUND: Children in Afghanistan live in dangerous areas, and have been exposed to traumatic events and chaotic education. Progress has been made on access to education for girls who were the most affected by traditional attitudes against engagement in education. OBJECTIVES: The objectives were to evaluate the mental health of Afghan children living in regions of conflict and the association of mental health with school attendance for girls and boys. METHOD: The study included 2707 school aged children in eight regions of Afghanistan (16 provinces) residing in households recruited through a multi-stage stratified cluster sampling strategy in 2017. The level of terrorist threat was evaluated by the intensity of terrorist attacks recorded that year in each province. Child mental health was assessed with the parental report Strengths and Difficulties Questionnaire (SDQ) along with information on school attendance, sociodemographic characteristics and geographic location. RESULTS: A total of 52.75% of children had scores above threshold for the SDQ total difficulties score, 39.19% for emotional difficulties, 51.98% for conduct challenges, and 15.37% for hyperactivity/inattention. Peer relationship problems were high (82.86%) and 12.38% reported that these problems impacted daily life. The level of terrorist threat was associated with SDQ total difficulties (Adjusted Odds Ratio [AOR] = 4.08, P < 0.0001), with youth in regions with high levels of terrorist threat more likely to have problems than youth in regions with low or medium levels of danger, independent of region and ethnicity. School attendance was negatively associated with emotional symptoms (AOR = 0.65, P < 0.0001) and mental health difficulties with impairment (AOR = 0.67, P = 0.007), but positively associated with peer relationships difficulties (AOR = 1.96, P > 0.0001). Conduct (AOR = 1.66, P < .0001) and SDQ total difficulties (AOR = 1.22, P = 0.019) were higher among boys. Overall, gender did not modify the relationship between school attendance and child mental health. CONCLUSION: Attending school is essential for children's mental health, across gender, and should be supported as a priority in Afghanistan despite the return of the Taliban.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.004 | 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".