Art for Empowerment : One-on-one art sessions with dementia patients
Bibliographic record
Abstract
The objective of this final project was to implement one-on-one art sessions with three geriatric dementia patients who weren't active in daily recreational activities. This activity-based final project could then be used by instructors who are working with the geriatric population. It was also an aim to explore the empowerment of these three dementia patients through the ‘Art for empowerment’ art sessions. This final project was implemented at the Bradford Valley long term care center in Ontario, Canada. \nThis final project was an activity-based project in which qualitative research methods of data gathering were used. A focused interview for the collection of data from the patients´ relatives and the observation of the one-on-one art sessions were used \n \nThe results showed that the patients momentarily experienced positive feelings from these sessions and clearly enjoyed them, these experiences were momentary because of the illness that they are suffering from. I found that during the sessions the patients were able to express themselves and have an experience of accomplishing something on their own, in their lives where their ability to proceed in everyday tasks is limited .
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".