Decision: Impact of household food insecurity and nutrition on depression and anxiety symptoms among adolescents living in rural Pakistan — R1/PR8
Bibliographic record
Abstract
BackgroundThis study investigates the links between dietary diversity, food insecurity and mental health (depression and anxiety) in adolescents from rural Pakistan. Adolescence is a critical time for developing mental health disorders, yet limited research exists on these issues in low- and middle-income countries (LMICs).MethodsThe study included 1,396 adolescents (ages 9–15) and assessed their mental health, nutrition and maternal well-being. Depression and anxiety were measured using standardized questionnaires, while dietary diversity and food insecurity were evaluated through household assessments. Incidence rate ratios assessed the relationship between nutrition and mental health.ResultsResults showed that 8.1% of boys and 10.2% of girls experienced depression, with anxiety rates ranging from 5.8% to 39.1%. Adolescents from households with higher dietary diversity had lower symptoms of depression and anxiety (IRRs:0.91–0.96), while those with higher food insecurity had increased symptoms (IRRs:1.24–1.86). Folate deficiency was associated with depressive symptoms, particularly in boys. Maternal mental health was observed to mediate the relationship between food insecurity and adolescent depression and anxiety.ConclusionsThe study highlights that improving maternal mental health and addressing nutritional deficiencies, particularly folate, may benefit adolescent well-being. Further research in other LMICs is needed to explore these associations and their mechanisms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".