Not Into Sex: Women’s Experiences of Treatment-Emergent Sexual Dysfunction
Bibliographic record
Abstract
Despite recent studies suggesting that treatment-emergent sexual dysfunction (TESD) in women is much more prevalent than previously thought, it is not often discussed between physicians and female patients prior to prescribing psychotropic medication. Missing from the available quantitative research on TESD are stories from the women themselves, their experiences with disclosure or lack thereof, and the impact TESD has had on their sense of self and in their relationships. Concerned that this could have a significant influence on women’s mental, emotional, and sexual health, we conducted a study where we interviewed 10 women who self-identified as experiencing TESD after taking psychotropic medications for their mental health. Semistructured, in-depth interviews were conducted, informed by critical feminist practice, and grounded in feminist standpoint theory. Transcripts were then analyzed using thematic analysis to demonstrate the impact TESD had on the lives of these women. Six themes emerged from the interviews: (1) inadequate disclosure about TESD from physicians, (2) gender-based difference in how TESD is discussed, (3) the experience of physical side effects, (4) emotional responses to side effects, (5) concerns about how the partners of women living with TESD experience it, and (6) the importance of knowledge sharing. We conclude this article with a discussion of how these stories fit within the larger social context.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".