Examining the mental health impacts of climate change on young people in Canada: a national cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Climate change poses a substantial threat to the mental wellbeing of young people. Population-level research is urgently needed to help inform policies and interventions to ensure that young people are not burdened by long-term mental health impacts from climate change. We sought to identify the prevalence, distribution, and factors associated with climate change-related mental and emotional health outcomes among young people (aged 13-34 years) in Canada. METHODS: This study is part of a larger cross-sectional survey, which examined mental and emotional health responses to climate change among individuals aged 13 years or older from across Canada. We used a multi-stage, multi-stratified random probability sampling procedure. Participants were randomly recruited through either an addressed letter or a telephone call. Online and telephone questionnaires were used to interview individuals in English, French, or Inuktitut between April 1, 2022, and March 31, 2023. Data were weighted by age and province using population estimates from Statistics Canada and analysed using descriptive statistics, factor analyses, and multivariable regression analyses. FINDINGS: The full survey included 2476 participants, with a subgroup of 409 young people. Of the 401 respondents who provided their gender identity, 215 (54%) identified as cisgender women, 167 (42%) identified as cisgender men, and 19 (5%) identified as non-binary. Preliminary results suggest that young people in Canada experience a wide range of climate-related emotional and mental health outcomes. More than 70% of respondents in the young people subgroup reported having at least mild levels of sadness, anger, worry, anxiety, concern, helplessness, hopelessness, or powerlessness related to climate change. The severity of climate-related emotional responses differed by gender, with non-binary respondents and cisgender women reporting higher average levels of distress than cisgender men. Regional differences were also observed, with northern regions and urban locations reporting more severe reactions. INTERPRETATION: This study builds on the understanding of the burden of climate change on the mental health of young people. If unaddressed, the impact of this burden could have long-standing and wide-reaching public health and related socioeconomic effects. FUNDING: Canadian Institutes of Health Research, ArcticNet, Social Sciences and Humanities Research Council Doctoral Fellowship, Izaak Walton Killam Memorial Scholarship, and Alberta Innovates Graduate Student Scholarship.
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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 | 0.006 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".