System Issues Leading to “Found-on-Floor” Incidents: A Multi-Incident Analysis
Bibliographic record
Abstract
BACKGROUND: Although attention to patient safety issues in the home care setting is growing, few studies have highlighted health system-level concerns that contribute to patient safety incidents in the home. Found-on-floor (FOF) incidents are a key patient safety issue that is unique to the home care setting and highlights a number of opportunities for system-level improvements to drive enhanced patient safety. METHODS: We completed a multi-incident analysis of FOF incidents documented in the electronic record system of a home health care agency in Toronto, Canada, for the course of 1 year between January 2012 and February 2013. RESULTS: Length of stay (LOS) was identified as the cross-cutting theme, illustrating the following 3 key issues: (1) in the short LOS group, a lack of information continuity led to missed fall risk information by home care professionals; (2) in the medium LOS group, a lack of personal support worker/carer training in fall prevention led to inadequate fall prevention activity; and (3) in the long LOS group, a lack of accountability policy at a system level led to a lack of fall risk assessment follow-up. CONCLUSIONS: Our study suggests that considering LOS in the home care sector helps expose key system-level issues enabling safety incidents such as FOF to occur. Our multi-incident analysis identified a number of opportunities for system-level changes that might improve fall prevention practice and reduce the likelihood of FOF incidents in the home. Specifically, investment in electronic health records that are functional across the continuum of care, further research and understanding of the training and skills of personal support workers, and enhanced incentives or more punitive approaches (depending on the circumstances) to ensure accountability in home safety will strengthen the home care sector and help prevent FOF incidents among older people.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".