Application of Quality Improvement Framework to reduce incidents of aggressive and abusive behaviours in an inpatient acquired brain injury unit.
Bibliographic record
Abstract
Incidents of challenging behaviours among patients with acquired brain injury (ABI) are exceptionally common in inpatient ABI rehabilitation units. Minimizing the occurrence of these incidents is of utmost importance to safeguard patient and staff safety and well-being. The aim of the current ongoing project is to apply a Quality Improvement Framework across three provincial ABI programs in Ontario to reduce the number of challenging behaviour incidents of patients. The authors engaged frontline staff in multiple PDSA cycles aimed at discovering the precipitating factors to challenging behaviour that was occurring at the participating facilities. Information from two structured qualitative data collection methods indicated multiple precipitating factors that influence challenging behaviour, although communicating choice options was identified as the most common precipitating factor. The authors created a measurement tool and Behaviour Skills Training protocol to communicate choice options to clients. Through baseline measures results indicated that there was potential for improvement of staff behaviour. Once the training protocol was piloted and then trained across multiple staff members, client engagement increased and the topography of the challenging behaviour became less harmful. Future direction of the project involves creating a training protocol that can be delivered across all participating site effectively and efficiently.
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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 | 0.029 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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".