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Record W4415697486 · doi:10.1080/0092623x.2025.2575384

Profiles of Traumatic Sexuality in Sex Therapy Patients: A Comparison of Interpersonal Trauma History and Sexual Well-Being

2025· article· en· W4415697486 on OpenAlexafffund
Nadia Willard Martel, Ateret Gewirtz‐Meydan, Shalie-Emma Vaillancourt, Martine Hébert, Natacha Godbout

Bibliographic record

VenueJournal of Sex & Marital Therapy · 2025
Typearticle
Languageen
FieldMedicine
TopicSexual function and dysfunction studies
Canadian institutionsUniversité du Québec à Montréal
FundersSocial Sciences and Humanities Research CouncilSocial Sciences and Humanities Research Council of Canada
KeywordsHuman sexualityShameInterpersonal communicationInterpersonal relationshipSex therapyTherapeutic relationshipSexual functioning

Abstract

fetched live from OpenAlex

This study explored profiles of traumatic sexuality (including six sex-related dimensions-dissociation, intrusiveness, shame and guilt, pleasing the other, interpersonal distress, and hypervigilance) and compared differences in sexual well-being and interpersonal trauma among the profiles. A latent profile analysis was performed on a clinical sample of 484 sex therapy patients consulting for sex and relationship related issues. Results identified three distinct profiles: Low Traumatic Sexuality (characterized by low to moderate scores, 87.8% of the sample), High Traumatic Sexuality (marked by elevated scores across all dimensions, 2.5%), and Dissociative-Pleasing Traumatic Sexuality (exhibiting a mixed score pattern, 9.7%). Comparative analyses indicated that these profiles were further distinguished by key variables, such as sexual well-being and interpersonal trauma. These findings underscore the complex ways in which interpersonal trauma shapes sexual experiences, highlighting the importance of individualized therapeutic approaches tailored to address specific trauma histories and support the restoration of healthy sexual functioning and overall well-being.

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)0.001
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 score0.175
Threshold uncertainty score0.599

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.044
GPT teacher head0.327
Teacher spread0.283 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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