Planting the seeds for success: A qualitative study exploring primary healthcare providers’ perceptions about medical cannabis
Bibliographic record
Abstract
BACKGROUND: In Canada, cannabis legalization altered the way that the public can access cannabis for medical purposes. However, Canadians still struggle with finding healthcare professionals (HCPs) who are involved in medical cannabis counselling and authorization. This raises questions about the barriers that are causing this breakdown in care. Our study explored the perceptions of primary care providers regarding cannabis in their practice. METHODS: Semi-structured interviews were conducted by Zoom with HCPs in Newfoundland and Labrador (NL) to discuss their experiences with medical and non-medical cannabis in practice. Family physicians and nurse practitioners who were practicing in primary care in NL were included. The interview guide and coding template were developed using the Theoretical Domains Framework (TDF). A thematic analysis across the TDF was then conducted. RESULTS: Twelve participants with diverse demographic backgrounds and experience levels were interviewed. Five main themes emerged including, knowledge acquisition, internal influences, patient influences, external HCP influences, and systemic influences. The TDF domain resulting in the greatest representation of codes was environmental context and resources. INTERPRETATION: The findings suggested that HCPs have significant knowledge gaps in authorizing medical cannabis, which limited their practice competence and confidence in this area. Referring patients to cannabis clinics, while enforcing harm-reduction strategies, was an interim option for patients to access cannabis for medical purposes. However, developing practice guidelines and educational resources were suggested as prominent facilitators to promote medical cannabis authorization within the healthcare system.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".