Cannabis use and perceptions among Canadians with Spinal Cord Injury
Bibliographic record
Abstract
Abstract Design: A cross-sectional study was conducted based on an online survey among Canadian adults with any level or severity of SCI Objectives: To understand aspects of cannabis use and perceptions among Canadians with spinal cord injury and describe the self-reported reasons and side effects of cannabis use. Setting: Parkwood Institute at St Joseph’s Health Care London, the Power Cord SCI rehabilitation program at Brock University. Methods: Participants were asked to complete a survey. Results: 136 individuals were screened for participation, and 80 participants were enrolled. Of these participants, the majority (n=41 [51.2%]), indicated that they had tried cannabis in their lifetime, while 30 (37.5%) were current users. There was a non-significant increase (p=0.13) in cannabis use from pre (n=26, 32.5%) to post-injury (n=34, 42.5%). The most common reason for post-injury use was reducing pain (36.3%) and improving sleep (30%). Participants reported cannabis being moderately effective for both pain reduction and sleep improvement. Side effects were relatively mild and uncommon with the most frequently reported being fatigue both pre (7.5%) and post (11.3%) injury. Smoking was the most popular method of using cannabis before the injury (27.5%), while the preferred method after the injury was consuming edibles (26.3%). Conclusions: Most participants who used cannabis before their injury continued using it after their injury. Participants reported recreational use before the injury, but they endorsed using cannabis to manage pain and medical conditions after injury and suggested that healthcare professionals should be aware of these findings. Additional research in this field is needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".