Development and Initial Validation of the Attitudes Towards Older Adult Sexuality in Long-term Care Scale (AOASLC)
Bibliographic record
Abstract
In long-term care facilities where older adults may reside, negative attitudes about later life sexuality can result in restrictive facility policies and staff behaviors that suppress residents’ rights to sexual expression. No assessment instrument specifically focuses on the sexual behaviors of long-term care residents and existing measures of attitudes toward older adult sexuality do not include sexual expression in long term care, nor do they assess a full range of sexual behaviors. We developed the Attitudes toward Older Adult Sexuality in Long-term Care Scale (AOASLC). A large, diverse sample of 295 community-dwelling adults in the United States completed an online survey through Amazon Mechanical Turk. The survey included the AOASLC and self-report measures of related constructs. Two-hundred-and-ninety-five participants completed the survey (Mage = 49.16, SD = 14.69, range = 18– 84 years). Of the sample, 50.2% identified as female, 49.5% identified as male, and one person identified as transgender male. An exploratory factor analysis indicated a two-factor structure. Factor 1 represented general attitudes toward sexual behaviors, and Factor 2 represented acceptability of various sexual behaviors. The instrument evidenced good reliability and validity. While further validation research is necessary, the AOASLC is a promising new measure.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".