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Record W4408276560 · doi:10.1093/jsxmed/qdaf025

Deep and Superficial Dyspareunia Questionnaire: a patient-reported outcome measure for genito-pelvic dyspareunia

2025· article· en· W4408276560 on OpenAlexaff
Nisha Marshall, Samantha L Levang, Yang Liu, Heather Noga, Catherine Allaire, Melanie Altas, Shauna Correia, Miriam Driscoll, Kirstie Merkt-Caprile, Ria Nishikawara, R. A. Weaver, A. Fuchsia Howard, Jessica Sutherland, Lori A. Brotto, Caroline F. Pukall, Paul J. Yong

Bibliographic record

VenueThe Journal of Sexual Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicSexual function and dysfunction studies
Canadian institutionsVancouver Coastal HealthWomen's Health Research InstituteQueen's UniversityUniversity of British Columbia
Fundersnot available
KeywordsPsychosocialExploratory factor analysisPromFocus groupConfirmatory factor analysisPatient-reported outcomePsychologyClinical psychologyCategorizationCognitionMedicinePsychometricsQuality of life (healthcare)Structural equation modelingPsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION: Dyspareunia affects 8%-22% of women worldwide and an unknown number of gender-diverse people. Dyspareunia is commonly categorized into deep and superficial subtypes based on pain location and underlying etiology; however, current assessment tools inadequately differentiate between pain locations. AIM: This study aimed to develop a patient-reported outcome measure (PROM) that independently assesses deep and superficial dyspareunia and its psychosocial correlates: the Deep and Superficial Dyspareunia Questionnaire (DSDQ). METHODS: The DSDQ development stages included item construction, categorization, review/revision, focus groups, cognitive interviews, final review, and factor analysis. Items were developed by reviewing pre-existing measures related to dyspareunia. Constructs of these measures were adapted to create items for the DSDQ. Developed items were categorized according to a conceptual framework. To review items, 4 patient partners, 2 gynecologists, and 1 psychiatrist participated in a modified eDelphi process. Next, 3 patient focus groups (n = 5, n = 3, n = 4), 1 clinician focus group (n = 2), and patient cognitive interviews (n = 15) were conducted over 2 rounds. A qualitative descriptive approach guided interview analysis, which informed DSDQ modifications and generated evidence of validity. Clinician-researchers (n = 4) and patient partners (n = 2) completed the final revision. Lastly, an exploratory factor analysis (EFA) and a confirmatory factor analysis (CFA) determined the most appropriate factor structure. OUTCOMES: Generated items, validity, factor structure. RESULTS: Fifty-nine pre-existing measures were reviewed to generate an initial pool of 163 items. Items created were categorized into domains for characteristics (pain quality, timing, location, and intensity) or psychosocial correlates (impact of pain on cognitions, affect, sexuality, and behavior). The eDelphi modified 40 items, added 23, and excluded 10. After the final review, 175 items were approved for psychometric analysis. The EFA supported a 103-item, 6-factor model. The CFA supported a 45-item, 6-factor model. Factors included: (1) Vaginal Opening Pain; (2) Deep Vaginal/Pelvic/Abdominal Pain; (3) Pain Interference; (4) Affect and Cognitions Related to Provoked Pain; (5) Sexual Distress Related to Sexual Well-being; and (6) Pain Self-efficacy. CLINICAL IMPLICATIONS: The DSDQ will aid diagnosis, treatment, and assessment of dyspareunia changes over time in research and clinical settings. STRENGTHS AND LIMITATIONS: Strengths of this work include DSDQ co-development with patient partners, multidisciplinary clinicians, and researchers, as well as the rigorous mixed-methods development. Limitations include demographic and clinical homogeneity of the patient samples and sample sizes for the EFA and CFA. CONCLUSIONS: The DSDQ is a 45-item measure intended to assess deep and superficial dyspareunia. Future psychometric evaluation will further establish validity and reliability evidence.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 head0.053
GPT teacher head0.328
Teacher spread0.275 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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