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Record W1988754972 · doi:10.1080/15374416.2011.581619

An Effectiveness Study of a Culturally Enriched School-Based CBT Anxiety Prevention Program

2011· article· en· W1988754972 on OpenAlexaff
Lynn D. Miller, Aviva Laye-Gindhu, Joanna Bennett, Yan Liu, Stephenie Gold, John S. March, Brent F. Olson, Vanessa Emily Waechtler

Bibliographic record

VenueJournal of Clinical Child & Adolescent Psychology · 2011
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAnxietyPsychologyClinical psychologyIntervention (counseling)FeelingMental healthPopulationCulturally appropriatePsychiatryGerontologyMedicineSocial psychology

Abstract

fetched live from OpenAlex

Anxiety disorders are prevalent in the school-aged population and are present across cultural groups. Scant research exists on culturally relevant prevention and intervention programs for mental health problems in the Aboriginal populations. An established cognitive behavioral program, FRIENDS for Life, was enriched to include content that was culturally relevant to Aboriginal students. Students (N = 533), including 192 students of Aboriginal background, participated in the cluster randomized control study. Data were collected three times over 1 year. A series of multilevel models were conducted to examine the effect of the culturally enriched FRIENDS program on anxiety. These analyses revealed that the FRIENDS program did not effectively reduce anxiety for the total sample or for Aboriginal children specifically. However, all students, regardless of intervention condition, Aboriginal status, or gender, reported a consistent decrease in feelings of anxiety over the 6-month study period.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.097
GPT teacher head0.451
Teacher spread0.354 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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

Citations71
Published2011
Admission routes1
Has abstractyes

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