Using the Creative Arts in Therapy and Healthcare
Bibliographic record
Abstract
Warren, Looking Backwards, Looking Forwards: A Preface and Introduction to Using the Creative Arts in Therapy and Healthcare. Warren, Guidelines, Preparations and Practical Hints: A Brief Checklist for Workshop Leaders. Warren, Don't Forget to Breathe and Smile: Breathing Exercises as Warm-ups for Activities in Healthcare Settings. Watling, James, Folklore and Ritual as a Basis for Creative Therapy. Nadeau, Using the Visual Arts to Expand Personal Creativity. Warren, Coaten, Dance: Developing Self-image and Self-expression Through Movement. Yon, Expanding Human Potential Through Music. Warren, Drama: Using the Imagination as a Stepping-stone for Personal Growth. Neill, Storymaking and Storytelling: Weaving the Fabric That Creates Our Lives. Welch, Creating Community: Ensembling Performance Using Masks, Puppets and Theatre. Rollins, Arts for Children in Hospitals: Helping to Put the Art Back in Medicine. Pointe, Serviss, Friends Arts in Healthcare Programs at The University of Alberta Hospital: Fostering a Healing Environment. Warren, Healing Laughter: The Role and Benefits of Clown-doctors Working in Hospitals and Healthcare. Schamberger, Songlines: Developing Innovative Arts Programs for Use with Children Who Are Visually Impaired or Brain Injured. Spitzer, Laughterboss: Introducing a New Position in Aged Care.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.010 |
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".