Association of youth climate worry with current and past mental health symptoms: A longitudinal population-based study
Bibliographic record
Abstract
INTRODUCTION: Young people are worried about climate change but the association between climate worry and current and past mental health has not been examined in population-based samples. OBJECTIVES: To examine 1) the prevalence of worry about climate change at age 23-years and its association with contemporaneous mental health symptoms, and 2) and adolescent mental health symptoms. METHODS: We used a Canadian population-based birth cohort (n=1325) to examine associations between 1) climate change at age 23-years and concurrent anxiety, depression, and suicidal behaviors, and 2) mental health at age 15 and 17 years defined as anxiety, depression, aggression-opposition, inattention-hyperactivity. We adjusted for participants’ socioeconomic status, childhood IQ, sex, and parental history of psychopathology. RESULTS: Most participants were worried about climate change: 190 (14.3%) were extremely worried, 553 (41.7%) were somewhat worried, 383 (28.9%) were very worried, and 199 (15.0%) were not at all worried. Worry about climate change was associated with significantly elevated contemporaneous anxiety, depression, and suicidal thoughts. In longitudinal analysis, adolescent anxiety was associated with higher climate change worry at age 23-years while adolescent aggression-opposition was associated with lower climate change worry. CONCLUSIONS: Worry about climate change is associated with contemporaneous mental health symptoms. However, longitudinal analysis suggests that this is largely explained by prior mental health, with adolescent anxiety symptoms linked with higher worry and aggression-opposition with lower worry. Future studies should aim to better define the dimensions of climate anxiety and track it alongside symptoms using prospective follow-up studies. DISCLOSURE OF INTEREST: None Declared
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".