Special Care Aides' Attitudes Toward Therapeutic Recreation for the Elderly in Long-Term Care
Bibliographic record
Abstract
There is great interest in enhancing the quality of life for residents in long-term care facilities and, in that regard, therapeutic recreation is receiving substantial attention. In order for residents to benefit from therapeutic recreation, it is important for all professionals in long-term care homes to have positive attitudes and behaviors toward therapeutic recreation. Special Care Aides have the most contact with elderly patients and are the largest segment of staff that works with the aged. Without the cooperation of Special Care Aides, therapeutic recreation activities will not be successful. The purpose of the present study was to examine Special Care Aides and Care Aides attitudes toward therapeutic recreation for the elderly in long-term care homes using Ajzen's (1991) Theory of Planned Behavior. Participants were 78 Special Care Aides and Care Aides from four long-term care homes. The Therapeutic Recreation Attitude Measurement(TRAM), a revised version of the Leisure Attitude Measure, was used to assess attitudes and behaviors toward therapeutic recreation for the elderly. Participants' intention to perform positive behaviors toward therapeutic recreation predicted their behavior. Participants' cognitive attitudes toward therapeutic recreation influenced participants' behavioral intentions. The implications of these findings for advancing the acceptance of therapeutic recreation activities were discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".