Prevalence and Predictors of Sexual Problems Among Midlife Canadian Adults: Results from a National Survey
Bibliographic record
Abstract
BACKGROUND: Sexual problems are common among midlife men and women, and studies have identified a range of demographic, health, and relational correlates. Research on prevalence of these sexual problems within Canada is sparse and is warranted given the unique context related to provision of health care services in contrast to other countries. AIM: We investigated sexual problems (women's low desire, orgasm difficulties, and vaginal pain, as well as men's low desire, erection difficulties, and ejaculation difficulties) and their correlates among a large sample of Canadian men and women aged 40-59 years. METHODS: A national sample of Canadians was recruited (N = 2,400). Prevalence statistics for the sexual problems, and odds ratios for correlates were computed using logistic regression to identify demographic, health, and behavioral correlates of men' and women's sexual problems. OUTCOMES: Self-reported experiences in the last 6-months of low desire, vaginal dryness, vaginal pain, and orgasm difficulties for women, and low desire, erectile difficulties, and ejaculation problems for men. RESULTS: Sexual problems were relatively common; low desire was the most common sexual problem, particularly for women, with 40% reporting low sexual desire in the last 6 months. Women who were post-menopausal were much more likely to report low desire, vaginal pain, and vaginal dryness. Low desire and erectile difficulties for men, and low desire and orgasm difficulties for women were significant predictors of overall happiness with sexual life. CLINICAL TRANSLATION: Given the prevalence and impact of sexual problems indicated in our study, physicians are encouraged to routinely assess for and treat these concerns. CONCLUSIONS: Strengths include a national sample of an understudied demographic category, midlife adults, and items consistent with other national studies of sexual problems. Causal or directional associations cannot be determined with these cross-sectional data. Results are largely consistent with previous national samples in the United States and the United Kingdom. Sexual problems are common among Canadian men and women, with many being associated with self-reported sexual happiness. Quinn-Nilas C, Milhausen RR, McKay A, et al. Prevalence and Predictors of Sexual Problems Among Midlife Canadian Adults: Results from a National Survey. J Sex Med 2018;15:873-879.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".