Women's needs for CAM information to manage menopausal symptoms
Bibliographic record
Abstract
OBJECTIVE: To identify the information needs of women regarding complementary and alternative medicine (CAM) treatment options to alleviate menopausal symptoms. METHODS: Self-administered questionnaires were mailed to women responding to notices posted in family physicians' offices and a women's health center. Survey questions addressed preferred topics, formats and sources of information; experiences with information searching; and what signified good, trustworthy information. RESULTS: The women in this study (n = 413) indicated several challenges including a lack of time to gather information, gaps in, and lack of, relevant information, and poor information quality. They expressed interest in information about the menopausal process, conventional and CAM treatment options, and the safety of treatments. Personal consultation with health-care professionals was the preferred way for obtaining information. The majority of women preferred evidence-based information but there was also a substantial number of women who chose to rely on 'softer' evidence such as personal accounts. These results suggested two different subgroups; however, the data indicate that these are not mutually exclusive since many respondents showed a preference for both types of information. CONCLUSIONS: Women feel they are not sufficiently informed to make safe decisions regarding CAM treatment options to alleviate menopausal symptoms. Family physicians are a trusted information source and have an important role in providing women with that information. Brochures containing evidence-based information and a list of newsletters or books that include personal accounts, available in physician's offices and during personal consultations at women's health centers, are offered as a possible solution. A website is another possibility for distributing this information.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".