Reducing falls among geriatric rehabilitation patients: a controlled clinical trial
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of an intervention programme to reduce falls among geriatric rehabilitation patients. DESIGN: Pre/post-test design with independent pre-test and matched post-test samples. SETTING: Inpatient geriatric wards in a rehabilitation hospital. PARTICIPANTS: Seventy-six matched pairs (n = 152) of geriatric rehabilitation patients from one control and one intervention ward participated in the study, and 36 nursing staff surveys were completed. INTERVENTION: The intervention programme was developed based on interviews and systematic reviews. Educational materials were distributed to patients and families, and preventive measures were implemented. MAIN OUTCOME MEASURES: The rates of falls before and after the intervention both within and between the wards were compared, and surveys were completed. RESULTS: The matched patients presented no significant differences on age, gender or medical conditions. The falls rates, proportion of fallers and length of stay was higher among those in the control ward (P< 0.043). The percentage of fallers and the rate of falls/1000 patient days were lower on the intervention ward after implementation: odds ratio (95% confidence interval) = -2.9 (-6.6, -1.2) and -1.8 (-6.0, 0.5). Thirty of 36 respondents considered the tool to be helpful and beneficial for use on other wards. CONCLUSION: The intervention programme was effective in reducing falls among geriatric rehabilitation patients.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".