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Record W2750267355 · doi:10.5539/res.v9n3p207

A Study on Sexual Unwellness in Old Age: Assessing a Cross-National Sample of Older Adults

2017· article· en· W2750267355 on OpenAlexvenueno aff
Sofia von Humboldt, Sara Silva, Isabel Leal

Bibliographic record

VenueReview of European Studies · 2017
Typearticle
Languageen
FieldHealth Professions
TopicAging, Elder Care, and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsAffectionPortuguesePsychologyPerspective (graphical)Human sexualityGerontologyPopulationSample (material)Clinical psychologyDevelopmental psychologyDemographyMedicineSocial psychologySociologyGender studies

Abstract

fetched live from OpenAlex

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.

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)0.005
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 score0.091
Threshold uncertainty score0.622

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.204
GPT teacher head0.535
Teacher spread0.331 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

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