Inclusivity and relevance of an online intervention for Sexual Interest/Arousal Disorder among LBQ+ women
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".