Feasibility of Implementing an Exercise Program in a Geriatric Assessment Unit: the SPRINT Program
Bibliographic record
Abstract
BACKGROUND: An exercise program involving patients, caregivers, and professionals, entitled SPecific Retraining in INTerdisciplinarity (SPRINT), has been developed to prevent functional decline during hospitalization of older patients. GOAL: Assess the feasibility of implementing SPRINT in the context of a Geriatric Assessment Unit (GAU). METHODS: GAU's health-care professionals were instructed with the SPRINT. All new patients were evaluated by a physiotherapist shortly after admission to validate the eligibility criteria and allocation category of exercises. Questionnaires on physical activities were filled out by professionals, patients, and caregivers at baseline and after intervention. Quantitative and qualitative information was collected on adherence to the program. RESULTS: SPRINT was applied to 19 of the 50 patients admitted during the three-month pilot study. A daily average of one exercise session per patient was performed, most frequently with a nurse (37%), physician (20%), care attendant (13%) or by the patient alone (22%). The caregivers participated only 4% of the time. Barriers and facilitators in applying SPRINT have been identified. CONCLUSIONS: SPRINT appears relevant and applicable within GAUs. Future studies should be conducted to assess its safety and effectiveness in preventing hospital-related functional decline.
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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 | 0.005 | 0.013 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".