The Different Types of Overtime Work in Nursing and Their Associations With Nurse and Patient Outcomes: A Cross-Sectional Study Protocol
Bibliographic record
Abstract
Introduction: Nurses often work overtime to fill the shortage of nurses, ensure continuity of care or prevent service breakdowns. Some studies show that working overtime has negative impacts on both nurse and patient outcomes, whereas others suggest that overtime has some beneficial outcomes for patients and nurses. Some authors suggest that these conflicting results across studies could be explained by the type of overtime performed by nurses, an aspect that has received scant research attention. Objective: We aim to examine the associations between the different types of overtime work (voluntary or mandatory), and nurses’ perceptions of nurse and patient outcomes. Method: A provincial electronic cross-sectional survey will be conducted in the province of Quebec, Canada, to examine the associations between nurse overtime work and both nurse and patient outcomes. Discussion and Research Spin-offs: This study will likely provide deeper insights about the different types of overtime and their impacts on both nurse and patient outcomes. This may inform nursing practices and guide nursing union representatives, directors of nursing and government decision-makers.
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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 | 0.035 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".