Through the big top: An exploratory study of circus-based artistic knowledge translation in rural healthcare services, Québec, Canada
Bibliographic record
Abstract
BACKGROUND: The conventional methods and strategies used for knowledge translation (KT) in academic research often fall short in effectively reaching stakeholders, such as citizens, practitioners, and decision makers, especially concerning complex healthcare issues. In response, a growing number of scholars have been embracing arts-based knowledge translation (ABKT) to target a more diverse audience with varying backgrounds and expectations. Despite the increased interest, utilization, and literature on arts-based knowledge translation over the past three decades, no studies have directly compared traditional knowledge translation with arts-based knowledge translation methods. Thus, our study aimed to evaluate and compare the impact of an arts-based knowledge translation intervention-a circus show-with two traditional knowledge translation interventions (webinar and research report) in terms of awareness, accessibility, engagement, advocacy/policy influence, and enjoyment. METHODS: To conduct this exploratory convergent mixed method study, we randomly assigned 162 participants to one of the three interventions. All three knowledge translation methods were used to translate the same research project: "Rural Emergency 360: Mobilization of decision-makers, healthcare professionals, patients, and citizens to improve healthcare and services in Quebec's rural emergency departments (UR360)." RESULTS: The findings revealed that the circus show outperformed the webinar and research report in terms of accessibility and enjoyment, while being equally effective in raising awareness, increasing engagement, and influencing advocacy/policy. Each intervention strategy demonstrates its unique array of strengths and weaknesses, with the circus show catering to a diverse audience, while the webinar and research report target more informed participants. These outcomes underscore the innovative and inclusive attributes of Arts-Based Knowledge translation, showcasing its capacity to facilitate researchers' engagement with a wider array of stakeholders across diverse contexts. CONCLUSION: As a relevant first step and a complementary asset, arts-based knowledge translation holds immense potential in increasing awareness and mobilization around crucial health issues.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".