Evaluation of a Novel Online Webinar for Health Care Practitioner Education on the Health Effects of Smoking Cannabis in the Airway
Bibliographic record
Abstract
INTRODUCTION: The rapid legalization of cannabis has led to a knowledge gap among health care practitioners (HCPs). This study aimed to evaluate a novel online webinar for HCP education on the health effects of smoking cannabis. METHODS: An educational activity was developed by a multidisciplinary panel of experts. The webinar was recorded for on-demand viewing. A 10-item knowledge test was created by the multidisciplinary panel with content validity and was administered pre- and posteducational activity. RESULTS: Six hundred seven HCPs participated. Pre- to posttest scores increased from 56.9% ± 23.9% to 63.5% ± 24.7% (P < .0001). The live group had a significantly higher improvement in scores (10.5% [7.1-13.8% 95% CI] (P < .0001)) than the on-demand group. In multivariable regression model, the following factors were associated with a greater improvement in scores: older age (P = .0074), physician occupation (P = .026), live mode of learning interacted with lower pretest score (P < .0001), and live mode of learning interacted with female gender (P = .001). Approximately three-quarters of participants rated the webinar as above average (44.8%) or outstanding (29.8%). DISCUSSION: This novel online educational activity increased knowledge and awareness of the health effects of smoking cannabis in the airway among HCPs and engaged learners virtually during the COVID-19 pandemic.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".