Headlining mental health in a changing climate: a systematic analysis of climate change and mental health news coverage from Canada and the United States
Bibliographic record
Abstract
Abstract Climate change exerts a profound and far-reaching toll on mental health, resulting in mental illness, diminished wellbeing, and strained social relations, with the severity of these impacts anticipated to intensify under future climate change. News coverage of climate-mental health issues can shape public perceptions and responses, and therefore understanding this coverage is vital for creating and implementing strategies to lessen climate-mental health risks. We systematically examined the extent, nature, and range of news articles from Canada and the United States that covered climate-mental health impacts. Five electronic databases were searched. Articles were included if they were print or online news stories that covered climate-mental health impacts and were published in English or French by sources in Canada or the United States between 2016 and 2020. A total of 1085 articles (642 unique stories) met the inclusion criteria and underwent data extraction and analysis using descriptive statistics and thematic analysis. Coverage of climate-mental health outcomes increased in both Canada and the United States from 2016 to 2019. There was, however, a substantial decrease in coverage by Canadian news outlets from 2019 to 2020. Further, although 468 different news outlets published at least one story on the topic, repeat coverage by any one news outlet was infrequent. Most opinion pieces presented climate-mental health impacts as legitimate or expressed support for people experiencing these issues. Roughly half of the included stories identified populations that are vulnerable to climate-mental health impacts, particularly children and young people. Additionally, approximately half of the included stories identified potential interventions or coping strategies to protect mental health, most often involving changes to individual lifestyles or behavior. More sustained coverage that focuses on protective factors, interventions, and coping strategies is needed, as this could help foster public understanding and drive action to reduce climate-mental health risks.
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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 | 0.008 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.039 | 0.056 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".