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Record W3160142154 · doi:10.1177/10497323211009194

Exploring Indigenous Ways of Coping After a Wildfire Disaster in Northern Alberta, Canada

2021· article· en· W3160142154 on OpenAlexafffundabout
Stephanie Montesanti, Kayla Fitzpatrick, Tara Azimi, Tara K. McGee, Bryan Fayant, Lorraine Albert

Bibliographic record

VenueQualitative Health Research · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsCumulative Environmental Management AssociationUniversity of Alberta
FundersCanadian Institute for Advanced Research
KeywordsIndigenousCoping (psychology)GeographyEnvironmental planningPsychologyEcologyPsychiatry

Abstract

fetched live from OpenAlex

In May 2016, a wildfire devastated a northern region of Alberta, Canada, resulting in negative consequences on physical and mental stress, social relationships, and overall resilience among Indigenous residents. Research on coping and managing stress following a disaster has failed to incorporate unique characteristics from Indigenous perspectives. Sharing circles were held in urban and rural community settings to capture: (a) Indigenous perspectives of coping, (b) individual and collective strengths that helped Indigenous residents and communities to cope during and after the wildfire, and (c) intergenerational experiences of coping from stress among Indigenous residents. Indigenous residents' experience with coping from the wildfire was shaped by: (a) heightened physical and emotional stress, (b) existing structural inequities, and (c) strong community cohesion and connection to culture. An unexpected outcome of this research was the therapeutic value of the sharing circles for participants to share their experience.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.403
GPT teacher head0.494
Teacher spread0.090 · 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.

Study designQualitative
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

Citations38
Published2021
Admission routes3
Has abstractyes

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