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Record W3200105039 · doi:10.1080/00224499.2021.1975624

Development and Initial Validation of the Attitudes Towards Older Adult Sexuality in Long-term Care Scale (AOASLC)

2021· article· en· W3200105039 on OpenAlexaff
Rachael Spalding, Emma Katz, E. Sandra Byers, Barry A. Edelstein

Bibliographic record

VenueThe Journal of Sex Research · 2021
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsHuman sexualityScale (ratio)PsychologyExploratory factor analysisClinical psychologyTransgenderSample (material)Sexual behaviorResidential careLong-term careGerontologyDevelopmental psychologyMedicinePsychometricsPsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.142
GPT teacher head0.507
Teacher spread0.365 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

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