Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it