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Record W1137973260 · doi:10.1176/appi.ps.201400256

Process Evaluation of an Early-Intervention Program for Mood and Anxiety Disorders Among Older Adolescents and Young Adults

2015· article· en· W1137973260 on OpenAlexaff
Elizabeth Osuch, Evelyn Vingilis, Carolyn Summerhurst, Christeen I. Forster, Erin E Ross, Andrew J. Wrath

Bibliographic record

VenuePsychiatric Services · 2015
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsAnxietyIntervention (counseling)MoodClinical psychologyMood disordersPsychologyPopulationPsychiatryPsychological interventionMedicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Research to determine the best approach for providing early intervention for mood and anxiety disorders is imperative. The authors describe a process evaluation of an early-intervention program for transition-age youths with mood or anxiety disorders. METHODS: Causal and logic models for pathways to care for the program, as well as descriptive data from 548 participating youths, are presented. Follow-up measures of functional improvement are reported. RESULTS: Diagnostic characterization, symptom severity, and functional impairment of participants indicated that the model selected an appropriate catchment population without creating excessive overinclusion. Self-referred youths reported greater anxiety and substance use. Acceptance by the program was predictive of greater follow-through with treatment. Several variables, including frequent lifetime marijuana use, predicted loss to follow-up. At follow-up, youths were significantly functionally improved. CONCLUSIONS: This process evaluation indicated that the model provided appropriate early intervention for youths with mood or anxiety disorders without causing excessive overinclusion.

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 score0.171
Threshold uncertainty score0.571

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 head0.012
GPT teacher head0.313
Teacher spread0.300 · 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

Citations16
Published2015
Admission routes1
Has abstractyes

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