Motivations and expectations for using cannabis products to treat pain in humans and dogs: a mixed methods study
Bibliographic record
Abstract
Abstract Background Social media and academic literature suggest that more people are using cannabis to treat their own or their dog’s chronic pain. This study identifies the reasons people use cannabis products to treat their own pain or their dog’s pain and explores whether these products have fulfilled their expectations. Methods An anonymous, online survey was used to collect quantitative and qualitative self-report data on respondents’ perceptions, motivations and expectations about their or their dog’s chronic pain and cannabis use. The analyses are based on U.S. adults who reported using cannabis products to treat their own ( N = 313) or their dog’s ( N = 204) chronic pain. Quantitative responses from the two groups were compared using Chi-Square tests and qualitative data were analyzed using a thematic analysis. Results Human patients and dog owners reported similar motivations for using cannabis products to treat chronic pain, with the more popular reasons being that cannabis products are natural, are preferred over conventional medication, are believed to be the best treatment or good treatment option for pain. Similar proportions of human patients and dog owners reported that the use of cannabis products fulfilled their expectations (86% vs. 82% respectively, χ 2 (1, 200) = .59, p = .32). The qualitative data revealed that their expectations were met by reducing pain, increasing relaxation, and improving sleep, coping, functionality and overall well being. Additionally, the qualitative data suggests that cannabis products offer a return to normalcy and a restored sense of self to human and dog patients. Conclusions The results suggest that people choose cannabis products because they are natural and a possible solution to managing chronic pain when conventional medicines have not been effective. Most people report that their expectations regarding pain management are fulfilled by these products. More accurate assessments are vital, however, for understanding both the objective biomedical and subjective socioemotional benefits of cannabis products for effective pain management for human and dog patients. In addition, objective factual information regarding cannabis products for effective pain management in humans and dogs is needed. It is recommended that both physicians and veterinarians work towards feeling more comfortable proactively broaching the subject of cannabis use with additional training and education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".