Overlapping Pandemic- and Climate-Related Worry: Prevalence and Association with Mental Health Outcomes in a Canadian Sample
Bibliographic record
Abstract
A growing body of research suggests the impacts of both the COVID-19 pandemic and climate change have negatively affected population mental health. However, evidence remains limited on the prevalence of overlapping pandemic- and climate-related worry and its association with mental health outcomes. The fourth round of the monitoring survey, Assessing the Impacts of COVID-19 on Mental Health, was administered to adults living in Canada, between November-December 2021, stratified and weighted by age, income, gender, and region. Respondents were asked about stressors related to the pandemic, including: “Worrying about the compounding effects of COVID-19 alongside the climate crisis”. Bivariate statistics and logistic regression were used to assess how responses to this question varied by sociodemographic characteristics and indicators of mental health. Overall, 3,030 respondents participated, with 36.1% endorsing overlapping pandemic- and climate-related worry. Prevalence varied significantly across sociodemographic characteristics, including gender, income, and disability status. Moreover, those who reported this worry were more likely to describe their current mental health as poor, to endorse suicidal ideation, and to be experiencing severe mental distress, even when controlling for pre-existing mental health conditions prior to the pandemic. These results suggest that overlapping pandemic- and climate-related worry is relatively common among adults in Canada. This reiterates the importance of attending to the social determinants of health when considering the mental health consequences of climate change and the pandemic.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".