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Record W4408740801 · doi:10.70256/361228lrwyji

Impacts of the 2013 Floods on Families' Mental Health in Alberta: Perspectives of Community Influencers and Service Providers in Rural Communities

2019· article· en· W4408740801 on OpenAlexaboutno aff
Nasreen Lalani, Julie Drolet

Bibliographic record

VenueBest practices in mental health · 2019
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsnot available
Fundersnot available
KeywordsInfluencer marketingMental healthService providerRural communityMental health serviceService (business)Environmental planningEnvironmental healthBusinessGeographySocioeconomicsEconomic growthSociologyPsychologyMedicineMarketingPsychiatry

Abstract

fetched live from OpenAlex

Disasters can have a detrimental impact on the physical, social, mental health, and well-being of individuals, families, and communities. The 2013 floods in Southern Alberta affected rural communities and created a sudden and substantial need for mental health services and resources. The Alberta Resilient Community Research Project was undertaken to understand the perspectives and experiences of community influencers who were engaged in the post-flood recovery process to promote resilience, mental health, recovery, and well-being for children and youth living in rural communities. Using the community-based participatory research approach, the authors interviewed community influencers and social service providers (n = 37) from various service organizations. To build a resilient community, the project implemented several promising practices to promote and foster the resilience of children, youth, and their communities post-flood in rural Southern Alberta. Recommendations will be provided for disaster preparedness and long-term recovery in rural communities.

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)0.003
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.072
GPT teacher head0.454
Teacher spread0.383 · 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 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

Citations5
Published2019
Admission routes1
Has abstractyes

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