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Record W2050317207 · doi:10.1080/14681990120040078

The effect of a cognitive-behavioral group treatment program on hypoactive sexual desire in women

2001· article· en· W2050317207 on OpenAlexafffund
Gilles Trudel, André Marchand, Marc Ravart, Sylvie Aubin, Lyse Turgeon, Pierre Fortier

Bibliographic record

VenueSexual & Relationship Therapy · 2001
Typearticle
Languageen
FieldMedicine
TopicSexual function and dysfunction studies
Canadian institutionsUniversité de MontréalInstitut universitaire en santé mentale de MontréalUniversité du Québec à Montréal
FundersHealth Canada
KeywordsCognitionPsychologyClinical psychologyHypoactive sexual desire disorderPsychotherapistCognitive behavioral therapySexual desirePsychiatryHuman sexuality

Abstract

fetched live from OpenAlex

The present paper describes the first extensive controlled study designed to assess and treat Hypoactive Sexual Desire Disorder (HSD) following an innovative, short-term, cognitive-behavioral group treatment program. HSD is well known to be among the most complex and difficult sexual disorders to treat. While the clinical literature reports positive treatment outcomes and descriptions of successful sex therapy techniques for sexual desire disorders, most of these are based primarily on single or multiple case studies following various therapeutic approaches. To date, there are no comprehensive controlled treatment outcome studies on the effect cognitive-behavioral treatment has on HSD. Results of this study are presented, as well as some descriptive information on women presenting with HSD. In general, results indicate that the treatment protocol is effective. It not only decreases the symptoms of this sexual disorder, but also improves overall cognitive, behavioral and marital functioning associated with HSD.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.098
GPT teacher head0.389
Teacher spread0.292 · 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

Citations191
Published2001
Admission routes2
Has abstractyes

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