Evidence map›Paper›PMID 40842672›Full record

ArticleInternational journal of wildland fire2025

Understanding rural adaptation to smoke from wildfires and forest management: insights for aligning approaches with community contexts.

Catrin M Edgeley, Jack T Burnett

Abstract read
In one paragraph

Article in International journal of wildland fire, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Catrin M EdgeleySchool of Forestry, Northern Arizona University, Flagstaff, AZ.
Jack T BurnettSchool of Forestry, Northern Arizona University, Flagstaff, AZ.

Funding

SSCIMA: Integrating Analysis of Socio-economic Sub-population Dynamics to Improve Spatial Models of Infectious DiseaseU54MD012388 · NIMHD · NORTHERN ARIZONA UNIVERSITY · PI Samantha Jane Sabo · 2017 to 2026
$49.7M
NIMHD NIH HHS U54 MD012388
6 · The paper itself

Abstract

Background: Rural communities are increasingly impacted by smoke produced by wildfires and forest management activties. Understanding local influences on smoke adaptation and mitigation is critical to social adaptation as fire risk continues to rise. Aims: We sought to determine the role of local social context in smoke adaptation and gauge interest in adaptation strategies that might reduce exposure. Methods: We conducted 46 semi-structured interviews with 56 residents and professionals in Parks, Arizona, USA, a rural community adjacent to public lands regularly affected by smoke. Key results: Rural residents think of smoke as an acceptable risk. Efforts to adapt to potential health impacts are minimal, though inaction is driven by diverse reasoning and tradeoffs. Local social context - particularly elements related to government distrust, forest management, and independence - heavily influences interest in uptake of different adaptation strategies as well as affecting access to, and interpretation of, information about smoke risks. Conclusions: Rural approaches to, and understandings of, smoke adaptation vary spatially and temporally. Public interest in broader forest management efforts can be leveraged to engage residents in conversations about proactive smoke adaptation. Implications: Smoke adaptation strategies in rural communities must meld evidence of their effectiveness with community preferences grounded in local context to overcome inaction.

Indexed as

community adaptationforest managementhealthprotective actionsrisk mitigationsmokesmoke adapted communitiessocial contextwildfire

Identifiers

PMID40842672
PMCPMC12366629

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.