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Record W2168658880 · doi:10.1258/mi.2009.009011

Menopause, libido and the Internet

2009· letter· en· W2168658880 on OpenAlexaboutno aff
Michael P. Cust

Bibliographic record

VenueMenopause international · 2009
Typeletter
Languageen
FieldMedicine
TopicSexual function and dysfunction studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLibidoMenopauseGynecologyThe InternetFamily medicineInternal medicineWorld Wide Web

Abstract

fetched live from OpenAlex

It is now nearly 40 years since the Internet was born. It was originally used in 1969 to network university computers in the United States of America. Since then, the introduction of email and the World Wide Web have made the Internet more widely available. Latest statistics suggest that there are nearly 1.5 billion users worldwide. The proportion of the population who are potential Internet users is now over 68% in the UK and over 72% in the USA. Australia achieves almost 80% penetration and Canada almost 85%. With ready access to such enormous numbers of people, researchers have seen the Internet as a potentially useful area for studying various populations. However, with Internet surveys, the problem of bias is difficult to overcome, particularly when the responders to any online survey are self-selected. The potential for bias arises because the Internet population may not be representative of a general population and the participants self-select (volunteer effect). In addition, there is often a low uptake of such surveys on the Internet, which further questions their validity. The use of a checklist for reporting Internet surveys (CHERRIES) has the potential to improve understanding and quality of such reports. By describing how the survey was performed, how the answering population was constituted and how it may differ from a randomly assigned population, we can judge the relevance of any particular report and be aware of potential biases. In this issue of Menopause International, the paper by Cumming et al. looks at the responses of women who accessed the menopause website (menopausematters.co.uk) to a questionnaire about their libido. Over 3000 responses were collected over 38 weeks and their results are reported. In line with other studies, this paper showed that sexual problems in women are common and increase with advancing age. It has been estimated that sexual problems affect one in two women overall. Sexual activity is known to decline with age. The commonest sexual problems reported are low sexual desire (43%), difficulty with vaginal lubrication (39%) and inability to climax (34%). In the paper by Cumming et al., almost 80% of periand postmenopausal women admitted to their libido being affected by the menopause, with most (86%) reporting a worsening, and 81% being distressed by this. Only 27% had discussed their problems with a health-care professional, although this was more common among postmenopausal rather than preor perimenopausal women and in those who were sexually active. Loss of libido is undoubtedly multi-factorial in origin and consequently no single treatment will be helpful for all. In this survey, it was clear that vaginal dryness was a factor in many women’s sexual problems but that they had not sought treatment. Hormone replacement therapy and testosterone replacement were helpful for some women, but not all. This study went on to offer the Brief Profile of Female Sexual Function (B-PFSF) questionnaire to see if women had hypoactive sexual desire disorder and empowered such women to seek help through their health-care provider. As long as account is taken of the selection bias such as in web-based surveys, there is little doubt that they represent a useful way of surveying ‘real people’ and as a tool to guide women with problems to an appropriate source of help.

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)0.003
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.022
GPT teacher head0.276
Teacher spread0.254 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2009
Admission routes1
Has abstractyes

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