Medicinal Cannabis, Chronic Pain and Sleep: Efficacy and Safety, Patients’ Perspectives, and Patterns of Use
Bibliographic record
Abstract
Chronic pain and sleep problems are two prevalent conditions frequently reported by the general population. Despite limited evidence, in recent decades, there has been a rapid rise in the use of Medicinal Cannabis (MC) for managing these two health conditions. Cannabis is increasingly used for therapeutic purposes by Canadians; however, this therapeutic option has largely emerged as a result of legal challenges instead of highquality empirical evidence establishing that the benefits exceed the harms. Furthermore, complicating the use of cannabis as a therapeutic product is its’ recreational use. Canada is the leading per capita consumer of cannabis for recreational use, which has raised concerns among some healthcare providers that patients may seek authorization to use MC for non-medical purposes. Therefore, the current thesis has examined three areas to inform the use of MC based on rigorous quantitative and qualitative approaches. It begins with investigating the efficacy and safety of MC and cannabinoids for impaired sleep through conducting a systematic review and meta-analysis of randomized clinical trials. Subsequently, it explores patients’ perspective towards MC use for chronic non-cancer pain (CNCP) using a qualitative approach and finally, it assesses declared rationale for cannabis use before and after legalization for recreational use for therapeutic purposes in Canada.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".