Climate change: The next challenge for public mental health?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Climate change is increasingly recognized as one of the greatest threats to human health of the 21st century, with consequences that mental health professionals are also likely to face. While physical health impacts have been increasingly emphasized in literature and practice, recent scholarly literature indicates that climate change and related weather events and environmental changes can profoundly impact psychological well-being and mental health through both direct and indirect pathways, particularly among those with pre-existing vulnerabilities or those living in ecologically sensitive areas. Although knowledge is still limited about the connections between climate change and mental health, evidence is indicating that impacts may be felt at both the individual and community levels, with mental health outcomes ranging from psychological distress, depression and anxiety, to increased addictions and suicide rates. Drawing on examples from diverse geographical areas, this article highlights some climate-sensitive impacts that may be encountered by mental health professionals. We then suggest potential avenues for public mental health in light of current and projected changes, in order to stimulate thought, debate, and action.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 | 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) | 0.002 | 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 it