Engaging heARTS: Developing an arts-based curriculum to enhance compassion and creativity in healthcare
Bibliographic record
Abstract
In 2014-15 Hamilton Health Sciences (HHS), with supportive funding from the Ontario Arts Council, partnered with a local group of professional artists to develop a curriculum called Awakening Your Creative Power: a seminar series on creativity, compassion and play. In order to understand the feasibility and impact of this arts-based curriculum for healthcare personnel working within a hospital, HHS undertook a comprehensive program evaluation. Input from participants who attended the course was obtained in three ways: (1) weekly evaluation forms at the end of each session (2) a final evaluation that asked questions about the overall seminar series, and (3) a focus group discussion held 2 weeks after the seminar series ended. This paper reports on the outcomes of the evaluation and the evolution of the curriculum over the past 3 years, including its impacts on both participants and the arts partners. The evaluation data demonstrate that the course was successful in meeting its stated objectives which include: enhancing interpersonal skills, fostering self-reflection, deepening compassion, cultivating resilience, recognizing creative potential, applying intention and coping with daily stresses through the power of play. In addition, the course also: increased self-awareness, fostered a sense of community, emphasized the value of creativity for its own sake, empowered participants, provided participants with a sense of accomplishment and made participants feel valued by the institution. The paper concludes with some reflections on the potential of engaging arts professionals in health professional education.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".