Self-management eHealth solutions for menopause – a systematic scoping review
Bibliographic record
Abstract
OBJECTIVE: The purpose of this scoping review was to highlight the current scientific evidence on eHealth-based information tools for menopause in terms of quality, requirements and previous intervention outcomes. METHODS: We systematically searched electronic databases (Embase, CINAHL, Cochrane Library, Global Health Database [Ovid], Web of Science, ClinicalTrials.gov [NLM], LIVIVO Search Portal [ZB MED] and Google Scholar) from 1974 to March 2022 for relevant records. RESULTS: Our search yielded 1773 records, of which 28 met our inclusion criteria. Thirteen of 28 selected studies were cross-sectional with qualitative content analysis of websites about menopause; 9 studies were cohort studies examining the impact of an eHealth intervention; two studies were randomized controlled trials comparing eHealth tools with conventional ones; and four studies were non-systematic literature reviews. CONCLUSION: This scoping review highlights the potential of eHealth-based information tools for the management of menopause and shows that most eHealth-based information tools are inadequate in terms of readability and the balanced view on information. Providers of eHealth-based information tools should pay attention to a participatory design, readability, balance of content and the use of multimedia tools for information delivery to improve understanding.
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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 | 0.030 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".