Lessons learned from using whiteboard videos and YouTube for deprescribing guidelines knowledge mobilization
Bibliographic record
Abstract
OBJECTIVES: Deprescribing is the planned and supervised process of dose reduction or stopping medication. Few clinical guidelines exist to help health care professionals in making decisions about deprescribing. The Bruyère Deprescribing Guidelines Team developed a series of evidence-based medication-class specific deprescribing guidelines and, to extend reach and uptake, disseminated them as whiteboard videos published on YouTube. This paper reports on the creation, sharing and evaluation of videos on proton pump inhibitor (PPI), antihyperglycemic (AHG), antipsychotic (AP) and benzodiazepine receptor agonist (BZRA) deprescribing guidelines. METHODS: Whiteboard videos depict an animator drawing on a whiteboard, while the narrator reads the script. In each video, the deprescribing algorithm is applied to mock patient cases. The videos were shared on YouTube and promoted via Twitter and other web-based tools. Evaluation methods included YouTube analytics and the validated Information Assessment Method (IAM) questionnaire. KEY FINDINGS: The four videos have a combined total of 26 387 views over the approximately 50 months since publishing, with viewers watching 34-40% of the videos' runtimes on average. The PPI and AHG deprescribing videos were viewed 4318 times in 97 countries during the first year. IAM respondents perceived the PPI, AHG and AP video content to be relevant, useful to learning and applicable to patient care. CONCLUSIONS: Using whiteboard videos on YouTube to explain deprescribing guidelines was a successful approach to knowledge mobilization. The evaluation approach is innovative as it combines typical success factors for online learning videos (e.g. views, estimated minutes watched) with responses to a validated information assessment tool.
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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 | 0.023 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".