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Enregistrement W2168658880 · doi:10.1258/mi.2009.009011

Menopause, libido and the Internet

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

Notice bibliographique

RevueMenopause international · 2009
Typeletter
Langueen
DomaineMedicine
ThématiqueSexual function and dysfunction studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineLibidoMenopauseGynecologyThe InternetFamily medicineInternal medicineWorld Wide Web

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,019
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil0,018

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,019
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0010,002
Communication savante0,0030,003
Science ouverte0,0010,001
Intégrité de la recherche0,0030,004
Charge utile insuffisante (le modèle a refusé de juger)0,0050,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,022
Tête enseignante GPT0,276
Écart entre enseignants0,254 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2009
Routes d'admission1
Résumé présentoui

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