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Record W4210309119 · doi:10.3138/cjhs.2021-0047

Examining sexually explicit material use in adults over the age of 65 years

2022· article· en· W4210309119 on OpenAlexaffvenueabout
Jordan Hardy, Tuuli M. Kukkonen, Robin R. Milhausen

Bibliographic record

VenueThe Canadian Journal of Human Sexuality · 2022
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsShameHuman sexualityPsychologyExploratory researchAmateurStigma (botany)CasualClinical psychologyGerontologyDemographyDevelopmental psychologyMedicineSocial psychologyGender studiesSociologyGeographyPolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

Older adult use of sexually explicit material (SEM) is under-researched. The current exploratory, cross-sectional study explored SEM use among adults 65 years and older. Eighty-eight participants from Canada and the United States completed a survey through Amazon Mechanical Turk to address questions on SEM use, access and barriers to use, and preferences in SEM. Results indicated that participants were accessing SEM 10.73 ( SD = 10.13) times per month and spending about 27.20 ( SD = 23.09) minutes per session. Users, on average, were more likely to be men. Results also indicated participants preferred mature, amateur, and MILF genres, and primarily accessed SEM online. Top barriers to SEM use were related to disapproval and shame, indicating that stigma around older adult sexuality may be present. Study findings expand upon knowledge in the field and help provide further understanding of older adults’ sexuality and SEM use.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.093
GPT teacher head0.334
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2022
Admission routes3
Has abstractyes

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