Evaluation of a Digital Educational Intervention to Enhance Oncology Nurse Professional Practice to Support Safe Cannabis Use: A Pilot Study
Bibliographic record
Abstract
INTRODUCTION: Young adults (aged 18-39) diagnosed with cancer often turn to cannabis to manage symptoms such as pain, anxiety, or cachexia. However, few educational interventions exist to train oncology nurses in supporting the safe use of cannabis in this population. METHODS: We conducted a two-arm pilot randomized controlled trial (1:1) to evaluate the feasibility and preliminary effects of a digital educational intervention grounded in the Theory of Planned Behavior. Nurses were randomized to either the intervention group ( n = 35) or an active control group ( n = 35). Feasibility outcomes included recruitment and retention rates, intervention uptake, module completion, participant engagement (eg, log-ins, time spent), and questionnaire completion. Nurses' knowledge, attitudes, self-efficacy, and intention to support safe cannabis use were measured at baseline and 1 month after randomization. Descriptive statistics summarized feasibility outcomes and sociodemographics; outcome changes were assessed using linear mixed effects models. RESULTS: Participants were mostly over 40 years old (60%) and 74% held a bachelor's degree. Of 70 participants enrolled, 57 completed the 1-month follow-up. In the intervention group, 89% (31/35) completed the full intervention. The intention to support safe cannabis use significantly increased in the intervention group compared to controls ( P = .016). DISCUSSION: This digital educational intervention demonstrated strong feasibility and preliminary effectiveness in enhancing nurses' intention to support safe cannabis use. Findings support the value of tailored, theory-informed educational strategies in oncology nursing and suggest the potential for scaling up in a larger trial.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".