A Study on Sexual Unwellness in Old Age: Assessing a Cross-National Sample of Older Adults
Bibliographic record
Abstract
Objectives: To analyze the contributors to Sexual Unwellness (SU) and to explore the latent constructs that can work as major determinants in SU for a cross-national older community-dwelling population.Methods: Study design: Complete data were available for 109 English and Portuguese older adults, aged between 65 and 87 years old (M=70.1, SD=5.99). Data was subjected to content analysis. Representation of the associations and latent constructs were analyzed by a Multiple Correspondence Analysis. A socio-demographic and health questionnaires were completed, assessing participants’ background information. Interviews were completed, focused on the contributors to SU.Results: The most frequent response of these participants was “lack of intimacy and affection” (25.1%) whereas “poor sexual health” was the least referred indicator of SU (11.2%). A two-dimension model formed by “poor affection, intimacy and sexual health”, and “poor general health and financial instability” was presented as a best-fit solution for English older adults. SU for Portuguese older adults were explained by a two-factor model: “daily hassles and health issues”, “poor intimacy and financial instability”.Conclusions: These outcomes uncovered the perspective of older adults concerning SU and the need of including these factors when considering the sexual well-being of older cross-national samples.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".