Associations between patient factors and adverse events in the home care setting: a secondary data analysis of two canadian adverse event studies
Bibliographic record
Abstract
BACKGROUND: Early identification of patients at who have a higher risk for the occurrence of harm can provide patient safety improvement opportunities. Patient factors contribute to adverse event occurrence. The study aim was to identify a single, parsimonious model of home care patient factors that, regardless of location and differences in home care program management and design factors, could provide a means of locating patients at higher and lower risk of harm. METHODS: Split modeling using secondary analyses of data from two recent Canadian home care patient safety studies was undertaken. Patient factors from the Minimum Data Set Resident Assessment Instrument (RAI) for Home Care and diagnoses consistent with ICD-10 and RAI-Mental Health assessment were used. Continuous and categorical measures of factors were considered. Adverse events were defined using World Health Organization taxonomy and measured on a dichotomous yes/no scale. Patient factors significantly associated (Pearson's Chi Square, p ≤ .05) with the occurrence of adverse events in both earlier studies were entered in forward selection regression analyses to locate factors predictive of adverse event occurrence. RESULTS: Instrumental activities of daily living dependency and escalating co-morbidity counts are associated with patient vulnerability to adverse events. CONCLUSIONS: Instrumental activities of daily living dependency and burden of illness, both easily identifiable early in the episode of care, are significantly associated with the risk of adverse event occurrence, however there is regional variability in the relationships.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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".