Incidence of adverse incidents in residential aged care
Bibliographic record
Abstract
Objective Adverse incident research within residential aged care facilities (RACFs) is increasing and there is growing awareness of safety and quality issues. However, large-scale evidence identifying specific areas of need and at-risk residents is lacking. This study used routinely collected incident management system data to quantify the types and rates of adverse incidents experienced by residents of RACFs. Methods A concurrent mixed-methods design was used to examine 3 years of incident management report data from 72 RACFs in New South Wales and the Australian Capital Territory. Qualitative thematic analysis of free-text incident descriptions was undertaken to group adverse incidents into categories. The rates and types of adverse incidents based on these categories were calculated and then compared using incidence rate ratios (IRRs). Results Deidentified records of 11 987 permanent residents (aged ≥65 years; mean (±s.d.) age 84 ± 8 years) from the facilities were included. Of the 60 268 adverse incidents, falls were the most common event (36%), followed by behaviour-related events (33%), other impacts and injuries (22%) and medication errors (9%). The number of adverse incidents per resident ranged from 0 (42%) to 171, with a median of 2. Women (IRR 0.804; P P Conclusion This study demonstrates that data already collected within electronic management systems can provide crucial baseline information about the risk levels that adverse incidents pose to older Australians living in RACFs. What is known about the topic? To date, research into aged care adverse incidents has typically focused on single incident types in small studies involving mitigation strategies. Little has been published quantifying the multiple adverse incidents experienced by residents of aged care facilities or reporting organisation-wide rates of adverse incidents. What does this paper add? This paper adds to the growing breadth of Australian aged care research by providing baseline information on the rates and types of adverse incidents in RACFs across a large and representative provider. What are the implications for practitioners? This research demonstrates that the wealth of data captured by aged care facilities' incident management information systems can be used to provide insight into areas of commonly occurring adverse incidents. Better use of this information could greatly enhance strategic planning of quality improvement activities and the care provided to residents.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".