A concept analysis of nursing overtime
Bibliographic record
Abstract
AIM: To report a concept analysis of nursing overtime. BACKGROUND: Economic constraints have resulted in hospital restructuring with the aim of reducing costs. These processes often target nurse staffing (the largest organizational expense) by increasing usage of alternative staffing strategies including overtime hours. Overtime is a multifaceted, poorly defined, and indiscriminately used concept. Analysis of nursing overtime is an important step towards development and propagation of appropriate staffing strategies and rigorous research. DESIGN: Concept analysis. DATA SOURCES: The search of electronic literature included indexes, grey literature, dictionaries, policy statements, contracts, glossaries and ancestry searching. Sources included were published between 1993-2012; dates were chosen in relation to increases in overtime hours used as a result of the healthcare structuring in the early 1990s. Approximately 65 documents met the inclusion criteria. REVIEW METHODS: Walker and Avant's methodology guided the analysis. DISCUSSION: Nursing overtime can be defined by four attributes: perception of choice or control over overtime hours worked; rewards or lack thereof; time off duty counts equally as much as time on duty; and disruption due to a lack of preparation. Antecedents of overtime arise from societal, organizational, and individual levels. The consequences of nursing overtime can be positive and negative, affecting organizations, nurses, and the patients they care for. CONCLUSION: This concept analysis clarifies the intricacies surrounding nursing overtime with recommendations to advance nursing research, practice, and policies. A nursing-specific middle-range theory was proposed to guide the understanding and study of nursing overtime.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".