Registered Nurses' Job Demands in Relation to Sitter Use
Bibliographic record
Abstract
BACKGROUND: Increases in overtime and absenteeism among registered nurses (RNs), in conjunction with a workforce having less experience, have resulted in high RN job demands. At the same time, there has been an increase in hospitals' use of patient sitters (i.e., unskilled attendants), but it is not known if these two changes are correlated. OBJECTIVE: The aim of this study was to determine if indicators of RN job demands, specifically overtime, absenteeism, and experience, are related to greater sitter use. METHOD: A nested case-control study design was used. All patients who were assigned a sitter (cases) were selected from a cohort of 43,212 medical and surgical patients who had been admitted to an academic health center in Montreal (Canada) in 2007 and 2008. For each case (n = 1,179), up to four controls (n = 4,167) were selected randomly among patients who did not receive a sitter. Multivariate logistic regression, within a generalized estimating equation framework, was used to assess the association between RN job demand indicators and sitter use, while controlling for other risk factors for sitter use. RESULTS: Compared with controls, patients who were assigned sitters had been subject to high rates of RN overtime and absenteeism and lower RN cumulative experience in the period prior to sitter use. Each additional hour of RN overtime increased the likelihood of sitter use by 108% (odds ratio = 2.08, 95% confidence interval = 1.32-3.29). Every 5 years of collective RN experience reduced the odds of sitter use by 23% (odds ratio = 0.77, 95% confidence interval = 0.66-0.89). Absenteeism was not associated with sitter use. DISCUSSION: High RN overtime and collective inexperience are associated with greater sitter use. A possible explanation is that sitters are used to palliate failures to meet high job demands. Further research is required to assess the impact of sitter use on patient outcomes.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".