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Record W2141839431 · doi:10.1177/14746514020020042301

Diabetes and sexual function in older adults: results of an international survey

2002· article· en· W2141839431 on OpenAlexaff
Alfredo Nicolosi, Dale B. Glasser, Gerald Brock, Edward O. Laumann, Clive Gingell

Bibliographic record

VenueThe British Journal of Diabetes · 2002
Typearticle
Languageen
FieldMedicine
TopicSexual function and dysfunction studies
Canadian institutionsSt Joseph's Health Centre
Fundersnot available
KeywordsMedicineDiabetes mellitusSexual functionSexual dysfunctionSexually activeSexual intercourseGerontologyDemographyPsychiatryPopulationInternal medicineFamily medicineEndocrinologyHuman immunodeficiency virus (HIV)

Abstract

fetched live from OpenAlex

The Global Study of Sexual Attitudes and Behaviors (GSSAB) is a contemporaneous survey of attitudes, behaviours, beliefs and satisfaction with relationships among men and women aged 40—80 years. Interviews were conducted with more than 26,000 subjects from 28 countries. Data captured from the survey indicated that the majority of adults are sexually active into their later years and that sex remains an important part of their overall life. This paper will discuss comparisons between diabetic and non-diabetic participants. Overall, approximately 10% of subjects reported that they had diabetes mellitus. Fewer diabetic than non-diabetic subjects reported that they had had sexual intercourse in the last 12 months and among sexually active subjects, diabetic subjects tended to report lower levels of physical and emotional satisfaction in their relationship with their partner. Symptoms of sexual dysfunction were more common among diabetic men than non-diabetic men. However, fewer than 15% of subjects had discussed their sexual problems with a doctor.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.254
Teacher spread0.230 · 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

Citations14
Published2002
Admission routes1
Has abstractyes

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