MétaCan
Menu
Back to cohort
Record W4413751652 · doi:10.2196/71301

Assessment of Mental and Chronic Health Conditions as Determinants of Health Care Needs and Digital Innovations for Women With Sexual Dysfunction: Cross-Sectional Population-Based Survey Study in Germany

2025· article· en· W4413751652 on OpenAlexvenueno aff
S M Kronthaler, Tatjana Tissen‐Diabaté, Maria Margarete Karsten, Jens‐Uwe Blohmer, Klaus M. Beier, Laura Hatzler

Bibliographic record

VenueJournal of Participatory Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicSexual function and dysfunction studies
Canadian institutionsnot available
FundersBerlin Institute of Health
KeywordsCross-sectional studyMental healthMedicineSexual dysfunctionEnvironmental healthPsychologyClinical psychologyPsychiatryGerontologyPathology

Abstract

fetched live from OpenAlex

Background: A chronic health condition (CHC) is a recognized risk factor for experiencing problems in sexual function (PSF). According to the International Classification of Diseases, 11th Revision (ICD-11), the development of severe symptoms of sexual distress is the defining criterion for clinically relevant sexual dysfunction. Data on the contribution of specific CHCs to clinically relevant sexual dysfunction symptoms and related health care needs are limited, hindering targeted interventions. Objective: This study examines the prevalence of PSF, sexual dysfunction, and sexual distress; assesses associations with CHC status; evaluates sexual dysfunction diagnoses; and explores health care preferences. Methods: Data collection in this cross-sectional population-based survey study was based on a questionnaire developed with patient and public involvement and administered by YouGov to a representative sample of adults in Germany. Analyses included 1970 women with and without CHCs and different CHC subgroups (mental health-related, gynecological, cardiovascular and metabolic, infectious and inflammatory, cancer, pain-related, and neurological). The outcomes measured were PSF, clinically relevant sexual dysfunction symptoms, sexual distress (Female Sexual Distress Scale-Desire/Arousal/Orgasm [FSDS-DAO]), and self-reported sexual dysfunction diagnoses. Multivariable regression and network analysis explored associations among CHC subgroups, PSF, sexual dysfunction, and FSDS-DAO scores. Results: Among 1970 cisgender women (mean age 49.6, SD 16.0 years), 1186 (60.2%) reported CHCs. The 6-month PSF prevalence was 75.2% (820/1090) in women with CHCs and 62.5% (399/638) in women without CHCs. Clinically relevant sexual dysfunction symptoms were less prevalent (CHC: 202/1046, 19.3% vs no CHC: 68/601, 11.3%). Multivariable regression models showed an association between sexual dysfunction and CHCs (odds ratio [OR] 2.56, 95% CI 1.90-3.49), which was the strongest for women with mental health-related CHCs (OR 2.31, 95% CI 1.70-3.13) and cancer CHCs (OR 2.00, 95% CI 1.45-2.78). Being in a relationship was a protective factor for clinically relevant distress among women with CHCs. Network analysis showed positive associations of PSF with gynecological and mental health-related CHCs and of sexual dysfunction with mental health-related, gynecological, and cancer CHCs. Women with sexual dysfunction symptoms reported low rates of sexual dysfunction diagnosis (CHC: 39/200, 19.4% vs no CHC: 6/55, 10.7%) and treatment (CHC: 16/146, 11.0% vs no CHC: 3/40, 7.0%). Gynecologists were the preferred health care providers for sexual dysfunction. The most commonly reported unmet need was a lack of information. Digital solutions, such as apps and websites with exercises, were desired as health care innovations. Conclusions: The burden of CHCs on women's sexual health extends beyond functional sexual impairment, with high rates of clinically relevant sexual distress. Cancer and mental health conditions are the strongest predictors of sexual dysfunction. Despite the high prevalence of sexual dysfunction in women with CHCs, access to diagnosis and treatment is limited. Digital offerings could help address these unmet needs.

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 score0.034
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.102
GPT teacher head0.466
Teacher spread0.364 · 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

Citations5
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Participatory MedicineSame topicSexual function and dysfunction studiesFrench-language works237,207