“Enough reality that you took it seriously, enough humour that you kept watching”: a qualitative analysis of student reception of <i>Larry Saves the Canadian healthcare system</i>
Bibliographic record
Abstract
Background Satire shows significant potential to engage audiences and effectively translate knowledge on politically-charged issues, yet applications to health-policy research are rare.Methods We present the qualitative component of a mixed-methods study evaluating the reception of a research-based digital musical satirizing health-system dysfunction. Following five university-based screenings of Larry Saves the Canadian Healthcare System, 16 students participated in semi-structured interviews to discuss their perceptions and interpretations.Results Four themes were identified via thematic analysis: Larry as a health-system primer, engagement through aesthetic appreciation, resonance through connections to personal experience, and hope for the future. Participants overwhelmingly appreciated the satirical approach, and described how it buttressed the show’s educational aims. Some reported learning new information, many indicated that the show reinforced existing knowledge, and most recommended it as a knowledge-translation tool for the public.Conclusions Findings open the door to more widespread use of satire as a genre of arts-based knowledge translation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.026 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| 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".