Integrative review: an evaluation of the methods used to explore the relationship between overtime and patient outcomes
Bibliographic record
Abstract
AIMS: To analyse, critically, methods employed to explore the relationship between nursing overtime and patient outcomes to strengthen future research. BACKGROUND: Nursing overtime hours have been increasing in the Western world since the 1980's; however, research detailing its implications for patient outcomes has not kept pace. Studies exploring the relationship between nursing overtime and patient outcomes have produced conflicting results and are deficient in number and rigour. DESIGN: Whittemore and Knafl's revised framework for integrative reviews guided the analysis. DATA SOURCES: A comprehensive multi-step search (1980-2012) of literature related to nursing overtime and patient outcomes in the CINAHL, Medline, PubMED, EMBASE and PsychInfo databases was performed. Reference lists and Google searches were completed for additional sources. Nine research papers met the inclusion criteria. REVIEW METHODS: All nine articles were included in the review. A systematic, iterative approach was used to extract and reduce the data to draw conclusions. RESULTS: There appears to be a positive relationship between nursing overtime and patient outcomes, however, eight of the nine studies revealed limitations in: (1) the definition and measurement of overtime; (2) data aggregation (organizationally and temporally) and (3) recognition or control of potential confounding variables. CONCLUSION: The quality in this research sample limits the ability of this body of work to be the basis of staffing policies. Future researchers need to be explicit in detailing their methods alongside a renewed commitment from administration to develop a tracking system of important parameters at the individual and bedside level.
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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 | 0.304 | 0.583 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.038 | 0.045 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".