Bibliographic record
Abstract
In the United Kingdom, and several other European countries, Canada, Australia, and the United States, art therapy is a state-registered health-care profession and its practitioners complete a postgraduate training for 2 years full-time or equivalent. The training encompasses models of psychotherapy, psychiatry, psychology, and the role and function of aesthetics and creativity in health care. Art therapy training consists of three core elements: the theoretical underpinnings of the practice, experiential engagement in artistic and interpersonal activities (so that trainees develop their capacity for self-reflection and insight and continue to engage in their own art-making) and clinical placements. Clinical placements are central to the training of art therapists, and in this way practitioners also learn about the roles of other health professionals, the function of interdisciplinary teamwork, and art therapy’s contribution to this. Professional registration of art therapists ensures that practitioners continue to maintain the standards of proficiency and professional practice established on qualification. In the United Kingdom, art therapy had its beginnings in the tuberculosis sanatoria of the 1940s but quickly developed within psychiatric and educational settings. Integrated with other care, it has since been widely incorporated into the fields of mental health and learning disabilities. However, there is a growing interest in art therapy with the medically and terminally ill. One recent survey in the UK found over 50% of art therapists in adult cancer care working with people in the palliative phase.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".