The Mental Health and Well-being Effects of Wildfire Smoke: A Scoping Review
Bibliographic record
Abstract
Abstract BackgroundSmoke from wildfires is a growing public health risk due to the enormous amount of smoke related pollution that is produced and can travel thousands of kilometers from its source. While many studies have documented the physical health harms of wildfire smoke, less is known about the effects on mental health and well-being. Understanding the effect of wildfire smoke on mental-health and well-being is crucial as the world enters a time in which wildfire smoke events can spread long distances past the immediate burn for prolonged periods of time. The aim of this scoping review is to review the existing information on wildfire smoke’s impact on mental health and well-being and to develop a model for understanding the pathways in which wildfire smoke may contribute to mental health distress. MethodsWe conducted searches using PubMed, Medline, Embase, Google, Scopus, and ProQuest for 1990-2022. These searches yielded 200 articles with 16 publications meeting inclusion criteria following screening and eligibility assessment and 3 more from the bibliographies of these articles were included for a total of 19 publication. ResultsOur review suggests that exposure to wildland smoke may have mental health impacts, particularly in episodes of chronic and persistent smoke events, but the evidence is inconsistent and limited. Qualitative studies disclose a wider range of impacts across multiple mental health and well-being domains. The potential pathways connecting wildfire smoke with mental health and well-being may operate at several levels including the physiological, physical, psychological, emotional, social, practical, and ecological. ConclusionsWe identify several priorities for future research: 1) applying more rigorous methods to generate more robust conclusions about the mental health and well-being impacts of wildfire smoke; 2) differentiating between mental illness or probable mental illness on the one hand and emotional well-being on the other; 3) identifying the contextual factors that set the stage for mental health and well-being effects, and; 4) identifying the causal processes that link wildfire smoke to mental health and well-being effects. The pathways model we provide can serve as a basis for further research and for mental-health and well-being protection interventions.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".