Lifting the curtain on the emergency department crisis: a multi-method reception study of Larry Saves the Canadian Healthcare System
Bibliographic record
Abstract
BACKGROUND: Despite growing evidence of the potential of arts-based modalities to translate knowledge and spark discussion on complex issues, applications to health policy are rare. This study explored the potential of a research-based theatrical video to increase public capacity and motivation to engage with the complex issues that make Emergency Department wait times such an intractable problem. METHODS: Larry Saves the Canadian Healthcare System is a digital musical micro-series developed from extensive research examining system-level causes of Emergency crowding and the ineffectiveness of prevailing approaches. We released individual episodes and a revised full-length version on YouTube, using organic promotion strategies and paid advertising. We used YouTube Analytics to track views, engagement and viewer demographics, and content-analyzed viewer comments. We also conducted five university-based screenings; 92 students completed questionnaires, rating Larry on 16 descriptors using a 7-point Likert scale. RESULTS: From June 2022 through May 2023, Larry garnered over 100,000 views (76,752 of the full-length version, 35,535 of episodes), 1329 likes, 2780 shares, and 139 comments. Views and watch time were higher among women and positively associated with age. Among YouTube comments, the predominating themes were praise for the video and criticism of the healthcare system. Many commenters applauded the show's accuracy, humor, and/or resonance with their experience; several shared healthcare horror stories. Students overwhelmingly agreed with all positive and disagreed with all negative descriptors, and nearly unanimously deemed the video informative, thought-provoking, and entertaining. Most also affirmed that it had increased their knowledge, interest, and confidence to participate in discussions about healthcare issues. Neither gender, primary language, nor employment in healthcare predicted ratings, but graduate students and those 25+ years old evaluated the video most positively. DISCUSSION: These findings highlight the promise of research-informed musical satire to inform and invigorate discourse on an urgent health policy problem. Larry has reached tens of thousands of viewers, garnered excellent feedback, and received high student ratings. Further research should directly assess educational and behavioural outcomes and explore what facilitative strategies could maximize this knowledge translation product's potential to foster informed, impactful policy dialogue.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".