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Record W4415166641 · doi:10.1080/19419899.2025.2570387

Inclusivity and relevance of an online intervention for Sexual Interest/Arousal Disorder among LBQ+ women

2025· article· en· W4415166641 on OpenAlexafffund
Kiarah M. K. O’Kane, Elizabeth A. Mahar, Kyle R. Stephenson, Lori A. Brotto

Bibliographic record

VenuePsychology and Sexuality · 2025
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsRelevance (law)Intervention (counseling)Sexual minorityHuman sexualityHuman Females

Abstract

fetched live from OpenAlex

LBQ+ women face barriers to treatment for Sexual Interest/Arousal Disorder (SIAD). Online interventions for SIAD may be a more accessible option for LBQ+ women than in-person treatment. However, limited research has examined LBQ+ women’s experiences of the relevance and inclusivity of online interventions for SIAD. Thus, this study investigated LBQ+ women’s perceptions of the relevance and inclusivity of an online intervention for SIAD, eSense. After using eSense, LBQ+ women (n = 14) reported their qualitative and quantitative perceptions of eSense’s relevance and inclusivity. Reflexive thematic analysis resulted in two overarching themes: Tension between the universality of sexual concerns versus the uniqueness of LBQ+ women’s experiences (subthemes: 1) eSense content can apply to anyone with SIAD; 2) eSense content did not fully capture the unique needs and experiences of LBQ+ women with SIAD; and 3) Partner gender shaped LBQ+ women’s experience with eSense); Looking for and perceiving cues of inclusivity and exclusion (subthemes: 1) Signals of inclusivity; 2) Cues of exclusion; and 3) The role of intersectionality in perceived inclusivity). Quantitative findings supported qualitative results. eSense was generally perceived as relevant and inclusive to LBQ+ women. Findings suggest that online interventions could address barriers to treatment for SIAD among LBQ+ women.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.493
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.056
GPT teacher head0.425
Teacher spread0.369 · 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.

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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