Work organization and patient care staff injuries: The impact of different care models for “alternate level of care” patients
Bibliographic record
Abstract
BACKGROUND: The number of elderly patients who do not have acute-care needs has increased in many North American hospitals. These alternate level care (ALC) patients are often cognitively impaired or physically dependent. The physical and psychosocial demands on caregivers may be growing with the increased presence of ALC patients leading to greater risk for injury among staff. METHODS: This prospective cohort study characterized several models for ALC care in four acute-care hospitals in British Columbia, Canada. A cohort of 2,854 patient care staff was identified and followed for 6 months. The association between ALC model of care and type and severity of injury was examined using multinomial and ordinal logistic regression. RESULTS: Regression models demonstrated that the workers on ALC/medical nursing units with "high" ALC patient loads and specialized geriatric assessment units had the greatest risk for injury and the greatest risk for incurring serious injury. Among staff caring for ALC patients, those on dedicated ALC units had the least risk for injury and the least risk for incurring serious injury. CONCLUSIONS: The way in which ALC care is organized in hospitals affects the risk and severity of injuries among patient care staff.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".