Incidence, criticality and typology of care-related adverse events in nursing homes based on initial epidemiological characteristics and possible prevention measures in France
Bibliographic record
Abstract
Knowledge of care-related adverse events in nursing homes in France is limited. An observational descriptive study was conducted in 25 nursing homes over a period of two weeks between 2016 and 2017. This study aimed to describe types of care-related adverse events and to assess their severity, the frequency with which they occurred, and their criticality. Eighty-six types of care-related adverse events, associated with 13 risk areas, were identified (31 of which were identified by an investigating physician). Of these types of events, 11 corresponded to an unacceptable level of criticality, and 13 were categorised as warranting surveillance. Efforts in nursing homes should focus on the different types of care-related adverse event: loss of or damage to a medical device, failure to administer medication, failure to coordinate between different establishments, shortfalls in planning and continuity of care, shortfalls in the information system, loss of or damage to laundry items, and unauthorised exit from the premises. Broad recommendations on preventing adverse events and improving nursing homes should be the subject of future 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".