Mental health and loneliness in Scottish schools: A multilevel analysis of data from the health behaviour in school‐aged children study
Bibliographic record
Abstract
BACKGROUND: Adolescent loneliness and poor mental health represent dual public health concerns. Yet, associations between loneliness and mental health, and critically, how these associations vary in school settings are less understood. AIMS: Framed by social-ecological theory, we aimed to identify key predictors of adolescent mental health and examine school-level variation in the relationship between loneliness and mental health. SAMPLE: Cross-sectional data on adolescents from the 2018 wave of the Health Behaviour in School-aged Children study (HBSC) in Scotland were used (N = 5286). METHODS: Mental health was measured as a composite variable containing items assessing nervousness, irritability, sleep difficulties and feeling low. Loneliness was measured via a single item assessing how often adolescents felt 'left out'. Multilevel models were used to identify social-ecological predictors of mental health, associations with loneliness and between-school variation. RESULTS: Loneliness, as well as demographic, social and school factors, was found to be associated with mental health. Mental health varied across schools, with the between-school difference greater among adolescents with high levels of loneliness. Additionally, the negative effect of loneliness on mental health was stronger in schools with lower average mental health scores. CONCLUSIONS: The findings suggest that schools can play an important role in shaping adolescent mental health. Our study uniquely identifies that school-based interventions targeting mental health may be especially necessary among lonely adolescents, and programmes aimed at tackling loneliness may be more beneficial in schools with poorer mental health.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".