Experiences of Canadian Women using Cannabis for Fibromyalgia Symptom Management: A Qualitative Description Study
Bibliographic record
Abstract
Fibromyalgia (FM) is a complex chronic pain disorder affecting approximately 3% of Canadians, predominantly women over the age of 40. Characterized by widespread pain, fatigue, cognitive impairments, and delayed diagnosis, FM often results in significant personal and healthcare challenges. Traditional pharmacological treatments are frequently ineffective, leading individuals to explore alternative therapies such as cannabis. Despite increasing legalization and accessibility in Canada, limited research has explored how women with FM use cannabis to manage their symptoms. To address this gap, a qualitative descriptive design was employed to capture the lived experiences of fifteen women with FM who currently or previously have used cannabis therapeutically. Participants were recruited from six Canadian provinces. Semi-structured interviews were conducted virtually via Zoom and analyzed using reflexive thematic analysis. Four key themes emerged: (1) Cannabis as a Core Component of Fibromyalgia Management – participants described cannabis as transformative, helping with pain, sleep, and quality of life; (2) Barriers and Challenges to Cannabis Use – participants experienced stigma, lack of medical guidance, high costs, and difficulties determining dosage; (3) Cannabis Use in Daily Life – participants emphasized intentional, self-monitored use to balance symptom management with daily responsibilities; and (4) Advice for Healthcare Professionals – participants advocated for nonjudgmental, patient-centered care that legitimizes cannabis as a viable treatment option. This study highlights the perceived value of cannabis in FM management and the persistent barriers faced by women using it for therapeutic purposes. Findings underscore the need for improved education among healthcare providers, increased affordability and accessibility of medical cannabis, and the promotion of collaborative, stigma-free care.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.024 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".