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Record W4294189748 · doi:10.1192/j.eurpsy.2022.1662

Sexual difficulties in old age and Person-Centered Therapy: A qualitative study with older adults

2022· article· en· W4294189748 on OpenAlexaff
Sofia von Humboldt, José Alberto Ribeiro-Gonçalves, Gavin Low, I. Leal

Bibliographic record

VenueEuropean Psychiatry · 2022
Typearticle
Languageen
FieldMedicine
TopicSexual function and dysfunction studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHuman sexualityPsychologyQualitative researchSexual functionPopulationAffect (linguistics)Clinical psychologySexual desireGerontologyDevelopmental psychologyMedicineGender studies

Abstract

fetched live from OpenAlex

Introduction Sexual well-being (SWB) of the older population can be significantly influenced by age (1) and sexual difficulties (2). Objectives Through qualitative research, this study focused on sexual themes that affect the SWB addressed by the older people in person-centered therapy. Methods Twenty-five older adults, aged between 65 and 82 years and residents on the community participated in this study. Results The results revealed eight main themes for these participants: Absence of a partner, family interference, dissatisfaction with the body, cleanliness and body care, problems in sexual function, physical violence, problems in sexual communication and fear of contracting sexually transmitted diseases. The most discussed themes were the absence of a partner, problems with sexual function and dissatisfaction with the body. Conclusions This study highlights the importance of exploring the sexual difficulties that the older population feels in relation to their SWB. 1.von Humboldt S et al. Sexual expression in old age: How older adults from different cultures express sexually? Sex Res Social Policy. 2020;1-15. 2.von Humboldt S et al. Are older adults satisfied with their sexuality? Outcomes from a cross-cultural study. Educ Gerontol. 2020;46:284-293. Disclosure No significant relationships.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.045
GPT teacher head0.301
Teacher spread0.256 · 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 designQualitative
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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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