RESEARCH Open Access It Can’t Hurt to Ask; A Patient-Centered Quality of Service Assessment of Health Canada’s Medical Cannabis Policy and Program
Bibliographic record
Abstract
Background: In 2001 Health Canada responded to a series of Ontario court decisions by creating the Marihuana Medical Access Division (MMAD) and the Marihuana Medical Access Regulations (MMAR). Although Health Canada has conducted a small number of stakeholder consultations, the federal government has never polled federally authorized cannabis patients. This study is an attempt to learn more about patient needs, challenges and experiences with the MMAD. Methods: Launched in the spring of 2007, Quality of Service Assessment of Health Canada’s Medical Cannabis Policy and Program pairs a 50 question online survey addressing the personal experiences of patients in the federal cannabis program with 25 semi-guided interviews. Data gathering for this study took place from April 2007 to Jan. 2008, eventually garnering survey responses from 100 federally-authorized users, which at the time represented about 5 % of the patients enrolled in Health Canada’s program. This paper presents the results of the survey portion of the study. Results: 8 % of respondents report getting their cannabis from Health Canada, while 66 % grow it for themselves.>50 % report that they frequent compassion clubs or dispensaries, which remain illegal and unregulated in Canada. 81 % of patients would chose certified organic methods of cultivation;>90 % state that not all strains are equally effective at relieving symptoms, and 97 % would prefer to obtain cannabis from a source where multiple strains are
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Scholarly communication Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Observational | low |
| gpt | Insufficient payload (model declined to judge) Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: yes | Not applicable | low |
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 | 0.020 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".