A Controlled Quality Improvement Trial to Reduce the Use of Physical Restraints in Older Hospitalized Adults
Bibliographic record
Abstract
OBJECTIVES: To implement and evaluate an evidence-informed multicomponent strategy to reduce physical restraint use in older adults admitted to acute care medical units. DESIGN: Stepped-wedge trial. SETTING: Four acute care medical units in Calgary, Alberta, over a 4-month time period. PARTICIPANTS: Data were collected from individuals aged 65 and older present on the study units during monthly restraint audits. INTERVENTION: Development of opinion leaders among the nursing leadership, education and training of physicians and unit nurses, and implementation of least restraint rounds. MEASUREMENTS: The primary outcome was rate of restraint use as determined from walk-around audits. Secondary outcomes included number of physician orders for physical restraints on the electronic medical record and fall reports. RESULTS: Thirteen percent to 27% of individuals were being restrained on the medical units before the intervention, with the vast majority of restraints being bed rails. This decreased to 7% to 14% after the intervention. The intervention resulted in a statistically significant reduction in restraint use measured in the early mornings (P = .01), and this trend continued after adjusting for unit and month (P = .06). Similarly, the rate of restraint use trended down at all other measured time periods but was not statistically significant. A limited number of individuals had an order for physical restraint within their electronic medical record (3% before, 2% after the intervention). The median number of monthly fall reports did not change (three before, three after; P = .60). CONCLUSION: A multicomponent team-focused quality improvement intervention has the potential to decrease the use of physical restraints in older hospitalized adults.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".