The patient woman or the woman patient? Examining the demographic differences between women seeking and not seeking treatment for sexual dysfunction
Bibliographic record
Abstract
Although female sexual dysfunctions affect a significant number of women around the world, the majority of those struggling do not ever seek professional treatment. As past research mostly focused on logistical and psychological barriers to help-seeking, there remains a gap in knowledge of what demographic characteristics potentially differentiate those individuals who do versus do not solicit support. The objective of this study was to explore a set of demographic variables and their links to treatment-seeking behaviours, including: age, ethnicity, education, income, employment, sexual orientation, relationship status, religion, and history of non-consensual sexual experiences. We used data from four completed studies that asked participants about their history of treatment-seeking, and our analytic sample included N = 869 self-identified women ( M = 31.41, SD = 11.30, range 19–78). Education, age, income, and employment significantly predicted help-seeking behaviours with treatment-seekers being more likely to be older, more educated, currently employed, and earning higher income. No significant associations were found for the other variables. Overall, the results demonstrate that there are notable demographic differences that separate help-seekers from non-seekers. As sexual well-being is an important aspect of one’s quality of life, risk factors that prevent people from seeking care for distressing sexual concerns should be a major public health priority. The limitations to the conclusions drawn in the paper due to data characteristics and analytic strategy are also discussed.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".