Taking Aim at Fall Injury Adverse Events: Best Practices and Organizational Change
Bibliographic record
Abstract
Fall injuries represent a huge healthcare, social and financial burden to the Canadian population. In 2004, the McGill University Health Centre (MUHC) was awarded recognition as a National Spotlight Organization for Implementation of the Registered Nurses Association of Ontario Best Practice Guidelines (BPGs). That same year, the author and co-leader of the Best Practice Guideline Program began the CHSRF Executive Training in Research Application (EXTRA) Program with the goal of reducing falls injuries, one of the most common adverse events in the MUHC and in acute care in Canada. This demonstration project used multiple strategies to strengthen a culture of safety and improve performance relating to adverse events, including: pilot testing several evidence-based falls prevention interventions (autumn 2005), training teams of champions to work across multiple sites, developing an infrastructure to support organizational change, modifying existing quality indicators to become benchmarkable, conducting a cost analysis of falls prevention, evaluating pre- and post-pilot surveys of organizational climate and obtaining initial baseline measures of the safety climate within the organization. Positive patient, practitioner and organizational outcomes suggest that falls safety prevention is feasible in large, complex healthcare organizations--and that safety is both a moral and a financial imperative. Next stages of the BPG program include full rollout, and measuring sustainability via a formal outcome evaluation study.
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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 | 0.034 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".