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Record W4313505653 · doi:10.1177/028072701503300202

Impacts of Wildfires on School Children: A Case Study of Slave Lake, Alberta, Canada

2015· article· en· W4313505653 on OpenAlexaffabout
Ivan Townshend, Olu Awosoga, Judith C. Kulig, Anna Pujadas Botey, Blythe Shepard, Bonita L. McFarlane

Bibliographic record

VenueInternational Journal of Mass Emergencies & Disasters · 2015
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsAlberta Health ServicesUniversity of Lethbridge
Fundersnot available
KeywordsPsychopathologyCoping (psychology)PsychologyClinical psychologyDemographyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Wildfires are becoming increasingly common, but there is limited understanding of their effects on children. This article reports on a study of wildfire impacts on children in a Canadian community. Self-reported measures of posttraumatic stress and coping and behavioral difficulties were obtained from a school-based survey of children in grades three to 12 carried out six months (T1) and 12 months (T2) after the fires. Students completed two screening instruments: the University of California at Los Angeles Post-traumatic Stress Disorder (PTSD) Reaction Index for Children and Adolescents – DSM-IV-TR, and the Strengths and Difficulties Questionnaire, and provided information about demographic details and loss of their home. Paired data (n = 140) revealed that a substantial number of children met certain PTSD criteria symptoms at T1 but the number declined by T2. Differences in symptoms by age, gender, and house loss were examined. Age and house loss were important differentiators of impact, but these waned through time, and house loss was not a defining trait of those most at risk of severe psychopathology. Future research examining children's responses to a variety of disasters would add to our knowledge about stress reactions while determining whether there are commonalities in responses across types of disasters.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.357
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2015
Admission routes2
Has abstractyes

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