Understanding Cannabis Use After Spinal Cord Injury: A Canadian Survey Study
Bibliographic record
Abstract
Objective: To describe the prevalence and characteristics of cannabis use among Canadians with spinal cord injury (SCI). Design: Observational study. Setting: Online survey. Participants: Canadian adults with any level or severity of SCI (N=80). In total, 136 individuals were screened for participation, of which 80 (61.2% men; mean age: 57.7y) completed the survey. Interventions: Not applicable. Main Outcome Measures: Prevalence and frequency of cannabis use; reasons for and against use; perceived effectiveness; adverse events; and modality of cannabis consumption. Results: Overall, 42.5% of participants reported cannabis use postinjury, and 37.5% were current users, exceeding the general Canadian population's usage rate (25%). Postinjury, the primary reasons for cannabis use shifted from recreation (25%) to alleviating pain (36.3%) and improving sleep (30%), with users perceiving moderate effectiveness. Reported adverse events were generally mild and infrequent, with fatigue being the most common (11.3%). Edibles replaced smoking as the most common modality of cannabis consumption postinjury. No significant associations were found between cannabis use and demographic variables, including sex, education, or employment status. Conclusions: This small survey highlights the prevalence and therapeutic motivations for cannabis use among Canadians with SCI, with a shift toward medicinal applications postinjury. With moderate perceived effectiveness and a low prevalence of only mild side effects, future research is warranted to evaluate cannabis' efficacy and safety in this population and to address barriers to its use, including stigma and health concerns.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".