The Influence of Functional Fitness and Cognitive Training of Physical Disabilities of Institutions
Bibliographic record
Abstract
According to an investigation done by Taiwan Ministry of the Interior in 2013, there was more than 90% of the disability care institutions mainly based on life care. Previous studies have shown that individuals can effectively improve physical and cognitive training, improved in independent living and everyday competence. The purpose of the study was to investigate influence of the intervention program applying functional fitness and cognitive training to disabled residents in the institution. The subjects were disabled persons of a care institution in southern Taiwan and were randomly divided into training and control groups, both having 17 subjects. The age of the subjects was between 56 and 98 years with a mean age of 79.08 ± 10.04 years; the subjects of training group implemented 12 weeks of training on physical and cognitive training, while the control group subjects did not have any training program. The results revealed that subjects of the training group have significantly improved their functional shoulder rotation flexibility of left and right anterior hip muscle group flexibility of right, sitting functional balance of left and right, naming, attention, delayed recall, orientation, and Montreal cognitive assessment (MOCA). The study suggested developing physical fitness programs and physical and cognitive prescriptions for the disabled people of the institutions.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".