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Record W2086600495 · doi:10.1080/16506070510008443

Healthcare Utilization Following Cognitive‐Behavioral Treatment for Panic Disorder with Agoraphobia

2005· article· en· W2086600495 on OpenAlexaff
Pasquale Roberge, André Marchand, Daniel Reinharz, Karine Cloutier, Nicole Mainguy, Jean‐Marc Miller, Jean Bégin, Julie Turcotte

Bibliographic record

VenueCognitive Behaviour Therapy · 2005
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsHôpital du Sacré-Cœur de MontréalInstitut universitaire en santé mentale de MontréalUniversité LavalUniversité du Québec à Montréal
Fundersnot available
KeywordsAgoraphobiaPanic disorderPsychologyCognitionPsychotherapistCognitive behavioral therapyClinical psychologyHealth carePanicPsychiatryAnxiety

Abstract

fetched live from OpenAlex

The aim of this study was to examine the overall changes in healthcare services utilization after providing an empirically supported cognitive-behavioral treatment for panic disorder with agoraphobia. Data on healthcare utilization were collected for a total of 84 adults meeting DSM-IV criteria. Participants were completers of a cognitive-behavioral treatment for panic disorder with agoraphobia. Data on utilization of healthcare services and medication were obtained from semi-structured interviews from baseline to 1-year after treatment. Results of the Friedman non-parametric analysis reveal a significant decrease in overall and mental health-related costs following treatment. This study shows a significant reduction in healthcare costs following cognitive behavior therapy for panic disorder with agoraphobia. More studies are needed to examine the potential long-term cost-offset effect of empirically supported treatments for panic disorder.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.162
GPT teacher head0.443
Teacher spread0.282 · 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 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

Citations31
Published2005
Admission routes1
Has abstractyes

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