Cannabis use for menopause in women aged 35 and over: a cross-sectional survey on usage patterns and perceptions in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVE: Use of cannabis for medical reasons has increased in Canada since legalisation of recreational cannabis in 2018. The objective of this study was to examine the pattern of use and perceptions about cannabis for menopause in women aged 35 and over in Alberta, Canada. DESIGN: Cross-sectional, web-based survey. SETTING: Online (location of participant residence in Alberta, Canada). PARTICIPANTS: Self-selected sample of women recruited through social media (Facebook, Instagram, Twitter) between October and December 2020. Inclusion criteria included: identified as woman, ages 35 and over, living in Alberta, Canada. PRIMARY AND SECONDARY OUTCOMES MEASURES: Self-reported data were collected on demographics, menopause status and symptoms, cannabis usage and how participants perceived cannabis. Descriptive statistics, comparative analysis and logistic regression explored relations in cannabis use and participant characteristics. RESULTS: Of 1761 responses collected, 1485 were included for analysis. Median age was 49 years; 35% were postmenopausal and 33% perimenopausal. Among analysed responses, 499 (34%) women reported currently using cannabis and 978 (66%) indicated ever using cannabis. Of the 499 current cannabis users, over 75% were using cannabis for medical purposes. Most common reasons for current use were sleep (65%), anxiety (45%) and muscle/joint achiness (33%). In current users, 74% indicated that cannabis was helpful for symptoms. Current cannabis users were more likely to report experiencing menopause symptoms compared with non-users. History of smoking and general health status were associated with current cannabis use. CONCLUSIONS: Some women are using cannabis for symptoms related to menopause. Further research is required to assess safety and efficacy of cannabis for managing menopause and develop clinical resources for women on cannabis and menopause.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".