Improvements in partner support predict sexual satisfaction among individuals with multiple sclerosis.
Bibliographic record
Abstract
PURPOSE: Sexual dysfunction and low sexual satisfaction are common among individuals with multiple sclerosis (MS); however, little is known about factors that influence sexual satisfaction within this population. As such, the purpose of this study was to investigate the extent to which changes in negative and positive partner support predict sexual satisfaction levels over time in individuals with MS. DESIGN: Eighty-one individuals with MS completed measures of sexual dysfunction, sexual satisfaction, partner social support, and depression. Data from baseline and posttreatment follow-up were obtained from a larger randomized clinical trial of telephone-administered psychotherapy for depression in a population with MS. Multiple regression analyses were conducted with change in overall sexual satisfaction from baseline to posttreatment as the outcome variable. RESULTS: After controlling for age, gender, sexual dysfunction, years diagnosed with MS, and depression severity, those with increased positive partner support reported significant improvement in sexual satisfaction over time (β = .50, p < .001), as did individuals with decreased negative partner support (β = .36, p < .01). CONCLUSIONS: Results provide evidence that both positive and negative partner support have a distinctive role in the outcome of sexual satisfaction for individuals with MS. Understanding the unique role of positive and negative forms of partner support on sexual satisfaction will help lead to future interventions to improve sexual satisfaction among couples.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".