Impact of Clinician Education on Emergency Department Mechanical Restraint: An Interrupted Time‐Series Study
Bibliographic record
Abstract
OBJECTIVE: Examine the impact of an education-based intervention co-designed with clinicians on attitudes towards and skills in caring for people with behaviours of concern in an emergency department (ED). METHODS: This 20-month interrupted time-series study (and nested 8-month before-and-after study) was conducted at a metropolitan hospital ED in Melbourne, Australia. The primary outcome was episodes of mechanical restraint before (1 January 2023 to 31 January 2023) and after (1 March 2024 to 30 September 2024) exposure of staff to an educational intervention. Secondary outcomes were staff injuries caused by patients, rate of offering voluntary medication for agitation, and medication timing before (1 October 2023 to 31 January 2024) and after (1 March 2024 to 30 June 2024) the intervention. RESULTS: Episodes of mechanical restraint were lower after the intervention (mean [SD] before: 5.5 [3.4] episodes per week; mean [SD] after: 2.5 [1.6] episodes per week; mean difference [95% CI]: 3.0 [1.7, 4.4] episodes per week, p < 0.001). There was a change in the level (β [95% CI] episodes per week: 2.22 [-4.35, -0.09], p = 0.041), but not a pre-existing downward trend (-0.00 [-0.07, 0.06], p = 0.960), of mechanical restraint. Staff sustained injuries caused by patients were lower after the intervention (φ = 0.138, p = 0.032). No significant difference was observed for offers of voluntary medication (φ = 0.049, p = 0.717) nor time to medication (mean difference [95% CI]: 67.08 [-83.87, 218.03] minutes, p = 0.374). CONCLUSIONS: A multifaceted educational intervention was associated with fewer episodes of emergency department mechanical restraint and fewer staff injuries caused by patients. Further work is needed to determine generalisability.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".