The Evolution of a Clown in Service: Reflections on Therapeutic Clowning in Canada
Bibliographic record
Abstract
Drawing on their experience as therapeutic clown practitioners, reflective practice facilitators, and trainers, the authors reflect thoughtfully on three key questions: What is the deeper nature of this relational practice called therapeutic clowning? Why are seasoned professional performers increasingly being drawn to this practice? What kinds of adaptations and considerations does a clown artist undertake to move from the world of theatre and performance to the world of health care and compassionate service? Beginning with a brief look at the evolution of therapeutic clowning in Canada and abroad, the authors turn to a deeper consideration of practices in pediatric, adult, and dementia-care settings. Child life and clown doctor pediatric models of therapeutic clown are delineated, as is the Elder-clown approach used by many practitioners working with older people in long-term care and community care. The authors emphasize the importance of reciprocity and co-created play between practitioner(s) and the people they serve and consider the role these play in bolstering resilience, vitality, and a sense of agency in the lives of vulnerable people in hospital and institutionalized care settings. The article also emphasizes that foundational clown training and performance experience alongside specialized training are crucial for therapeutic clown practitioners. Throughout, poignant vignettes illustrate and illuminate the complex, challenging, and rewarding dimensions of therapeutic clown practice.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.038 | 0.027 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".