Mental health during wildfires in Southcentral Alaska: An assessment of community-derived mental health categories, interventions, and implementation considerations
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
Previous studies have linked wildfires to a range of adverse mental health outcomes, but there has been limited research on the mental health impacts of wildfire in Alaska, an area undergoing rapid environmental change. We used a multi-level qualitative approach to identify mental health and psychosocial problems, coping, existing support, and gaps in support among communities who were affected by the Swan Lake and McKinley fires in Alaska in 2019. We recruited 39 community members from Anchorage and the Kenai Peninsula to participate in free list interviews, a community ranking workshop, and in-depth interviews, and we recruited 12 professional key informants including wildland firefighters, mental health providers, community advocates, policy makers, and public health professionals to participate in in-depth interviews and a discussion-based workshop. There were several locally-defined categories of mental health issues identified in relation to wildfires in southcentral Alaska in 2019. Key informants who work in the region identified a package of communications-related interventions as being the most impactful and actionable support for wildfire-related mental health concerns. Additional highly rated mental health supports centered around leadership acknowledging the connection between wildfire and mental health, connecting community members to formal or informal systems of mental health care, enhancing the emergency shelter system, and providing crises debriefing during wildfire evacuations. The results of this study can be utilized to facilitate implementation of prevention and response activities to support mental health resilience during wildfires in Alaska and other wildfire-affected regions.
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 | 0.001 | 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 it