Bibliographic record
Abstract
Objectives: Participants will learn how art therapists can achieve individual or group goals through the use of art materials and processes. They will also learn how organizational goals can be incorporated into relevant art making experiences. The presentation begins with a brief overview of art therapy and the history of mandala making in community building. We will then review the mandala-making staff workshops. The discussion will close with an exploration of how this process may be applied to other work environments.Methods: These workshops were originally offered to our long-term care staff on eight different occasions during, "Planetree Month" in May of 2011, a month dedicated to staff self-care. The “Planetree Month” planning committee asked the art therapist to develop a team-building exercise that would be fun, creative and completed within 45 minutes. The committee also hoped that the workshop participants, who had little or no artistic experience or skill, would make a collective art work (within the time constraints,) that would be a source of pride for the participants and be good enough to be installed on the walls of the centre as a testament to the teamwork achieved.Results: The goals of the workshop were accomplished through the careful design of this workshop. More than 200 staff members participated, embraced the process and completed eight mandalas which are now permanently displayed on the walls of Donald Berman Maimonides Geriatric Centre. Creativity can bring out the best in people, creating synergy and positive community growth.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".