Reliability of a self-reported questionnaire assessing the use of cannabis products to treat chronic pain
Bibliographic record
Abstract
PURPOSE: To develop a questionnaire measuring the use of cannabis to manage chronic pain and assess its reliability. DESIGN: A questionnaire was developed based on a review of the literature and input from an expert panel. A longitudinal survey was conducted to assess its reliability. METHODS: A preliminary questionnaire assessing pain conditions, the type of cannabis products used, the methods of use, the concentration of main cannabinoids, and the use of other pain therapies was developed. The expert panel reviewed the items of this preliminary version to enhance its content validity. The longitudinal survey was conducted to assess test-retest reliability. A two-round survey with 158 participants with chronic pain was conducted between November 2023 and January 2024. Kappa and weighted kappa coefficients were calculated to assess the agreement between the responses of the two rounds. According to criteria of Landis and Koch, items with a kappa ≥ 0.61 were considered to have high reliability. FINDINGS: A 24-item questionnaire was developed and tested. The average age of participants was 38 years, and 53.5% were female. Most of the items assessing the use of cannabis products yielded high reliability. Items with moderate reliability (0.41 ≤ Kappa < 0.61) included the type of pain and the type of cannabinoids used. CONCLUSIONS: The results suggest that the questionnaire developed in this study is a reliable tool for assessing cannabis use in patients with chronic pain in clinical or research settings.
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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".