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Record W4321495806 · doi:10.1002/jclp.23497

The effect of an Internet‐based cognitive behavioral therapy intervention on social support in disaster evacuees

2023· article· en· W4321495806 on OpenAlexafffund
Émilie Frenette, Marie‐Christine Ouellet, Stéphane Guay, Jessica Lebel, Vera Békés, Geneviève Belleville

Bibliographic record

VenueJournal of Clinical Psychology · 2023
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsUniversité de MontréalInternational Centre for Comparative CriminologyUniversité Laval
FundersCanadian Institutes of Health ResearchAlberta Innovates
KeywordsPsychologyIntervention (counseling)The InternetPsychotherapistCognitive behavioral therapyCognitionCrisis interventionClinical psychologyCognitive therapyApplied psychologyPsychiatryWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

INTRODUCTION: Both exposure to a natural disaster and psychological symptoms may lead to decreases in social support. Few studies have examined ways to improve social support among victims of natural disasters. AIMS: The objective of the study was to assess emotional and tangible support following a 12-session Internet-based cognitive behavioral therapy (ICBT) targeting posttraumatic stress (PTS), insomnia, and depression symptoms and to examine the association between posttreatment symptoms and emotional and tangible support. MATERIALS AND METHODS: One hundred and seventy-eight wildfire evacuees with significant PTS, depression and/or insomnia symptoms were given access to the ICBT. They completed questionnaires at pre- and posttreatment to measure social support and symptom severity. RESULTS: Results show that completion of the treatment led to an improvement in emotional support. Lower posttreatment PTS and insomnia symptoms were associated with higher posttreatment emotional support. CONCLUSION: ICBT may contribute to enhance emotional support through symptom improvement and probably more so when social support is address directly in treatment.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.764
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.444
GPT teacher head0.658
Teacher spread0.214 · 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 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

Citations4
Published2023
Admission routes2
Has abstractyes

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