Medicinal marijuana use: experiences of people with multiple sclerosis.
Bibliographic record
Abstract
OBJECTIVE: To describe medical marijuana use from the perspectives of patients with multiple sclerosis. DESIGN: A qualitative, descriptive design was used. Participants discussed their medicinal marijuana use in one-to-one, semistructured interviews. SETTING: Interviews were conducted at a time and place convenient to participants. PARTICIPANTS: Six men and eight women with multiple sclerosis participated. METHOD: Potential participants identified themselves to the researcher after receiving an invitation in a mailed survey. Eligibility was confirmed, and purposive sampling was used to recruit subjects. A range of issues emerged from the interviews. Interviews and data analysis continued until saturation occurred. MAIN FINDINGS: Descriptions fell into three broad areas: patterns of use, legal or social concerns, and perceived effects. Consumption patterns ranged from very infrequent to very regular and were influenced by symptoms, social factors, and supply. Legal concerns expressed by most respondents were negligible. Social concerns centred on to whom use was revealed. The perceived benefits of use were consistent with previous reports in the literature: reduction in pain, spasms, tremors, nausea, numbness, sleep problems, bladder and bowel problems, and fatigue and improved mood, ability to eat and drink, ability to write, and sexual functioning. Adverse effects included problems with cognition, balance, and fatigue and the feeling of being high. Although participants described risks associated with using marijuana, the benefits they derived made the risks acceptable. CONCLUSION: Further research is needed to clarify the safety and efficacy of marijuana use by patients with multiple sclerosis. If evidence of benefit is seen, medicinal marijuana should be made available to patients who could benefit from it. Until then, discussing medicinal marijuana use with patients will be awkward for health professionals.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".