Patient and nurse staffing characteristics associated with high sitter use costs
Bibliographic record
Abstract
AIM: This paper is a report of a study of the relationships between patient health conditions, nurse staffing characteristics and high sitter use costs. BACKGROUND: Increasing recourse to patient sitters is a major cost concern to hospitals. To reduce these expenses, we need to understand better the factors associated with high sitter use costs. METHODS: From a cohort of 43,212 medical/surgical patients admitted to an academic health centre in Montreal (Canada) in 2007 and 2008, all 1151 patients who received a sitter were selected. We applied multivariate logistic regression, using the Generalized Estimating Equation framework, to estimate the relationships between patient health conditions, nurse staffing characteristics and being in the upper two quintiles of sitter costs, vs. the lower three. RESULTS: The median sitter cost per patient, in Canadian dollars, was $772·35 (IQR = $1737·84); and $2397·00 (IQR = $3085·03) among the patients with high sitter use costs. In multivariate analyses, dementia, delirium and other cognitive impairments (OR = 1·49; 95% CI = 1·01-2·22) and schizophrenia and other psychoses (OR = 2·42; 95% CI = 1·08-5·76) increased the likelihood of high sitter use costs. In addition, every additional worked hour per patient per day by Registered Nurses (OR =0·33; 95% CI = 0·27-0·39) and by patient care assistants (OR = 0·11; 95% CI = 0·08-0·15) reduced the likelihood of high sitter use costs. Conclusion. Circumstances of understaffing and patients having psycho-geriatric conditions are associated with high sitter use costs. Improving staffing and providing additional resources to support the care of psycho-geriatric patients may lower these expenses.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".