Evaluating and Motivating Activation in Long Term Care: Lessons From a Pilot Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Older adults living in long-term care facilities typically receive insufficient exercise and have long periods of the day when they are not doing anything other than sitting or lying down, watching television, or ruminating (Wilkinson et al., 2017). We developed an intervention called the Experiential Centivizer, which provides residents with opportunities to use a driving simulator, watch world travel videos, and engage in exercise. We assessed the impact of the intervention on residents of a long-term care home in Fredericton, NB, Canada. In this paper, we report on the results observed and highlight the lessons learned from implementing a technological intervention within a long-term care setting. Practical and research recommendations are also discussed to facilitate future intervention implementation in long-term care.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it