Animal assisted therapy for elderly residents of a skilled nursing facility
Bibliographic record
Abstract
There is a growing population of those with dementia and other cognitive impairments that affect the quality of life. This is attributed to advances in science, technology and medicine leading to reductions in maternal mortality, infectious and parasitic diseases, occupational safety measures, and improvements in nutrition and education of the global population. According to the Administration on Aging (AoA), an agency of the U.S. Department of Health and Human Services in 2000, approximately 605 million people were 60 years or older. By 2050, that number is expected to be close to 2 billion. Animal assisted therapy (AAT) has been used as a therapeutic activity among the elderly to help improve well being and quality of life, but there has been limited research to demonstrate its effectiveness among those with dementia. The purpose of this study was to compare the effectiveness of AAT versus human interaction only on social behaviors and engagement among elderly patients with dementia in long-term care facility. Following random assignment to groups, the participants experienced two visits per week over a two-week time period of either animal therapy visits or human interaction visits. One week with no activities then followed then with alternate animal therapy and human interaction visits. The human interaction visits consisted of conversation and reading from and looking at pictures in a newspaper. During animal visits, participants were encouraged to touch, pet, brush, and talk to the dogs. In this study, AAT increased positive social behaviors resulting in fewer incidents requiring staff intervention. AAT coincides with current goals in long-term care settings - improving and enhancing socialization behaviors among older adults with dementia.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".