Advocating for the Inclusion of Fine Arts in the Healthcare Education Curricula – The Lived Experience of an Art-Loving Healthcare Educator
Bibliographic record
Abstract
The incorporation of fine arts into healthcare education curricula could enrich students' overall learning experiences, enhance cognitive and emotional development, and prepare them for the diverse, dynamic, and stressful workplace. In this paper, I will offer my lived experience of using the arts in to engage nursing and health sciences students’ participation. The paper begins with a brief definition and scope of the arts to anchor the discussion and a variegated collection of literature scan. I will present the various cognitive and professional benefits of integrating the arts into healthcare curricula. I will append photos of several past and contemporary international artists’ works, including personal communications that would reinforce my assertion. In addition, I will include anecdotes from students and others who shared their insights on the integration of the arts in learning. I will also discuss practice implications and recommendations for including the arts in curriculum development.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.028 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".