Effects of shift length on quality of patient care and health provider outcomes: systematic review
Bibliographic record
Abstract
BACKGROUND: Healthcare providers work increasingly under a variety of shift work systems to cover the continuous care required by patients. However, the effects of shift work on patient and provider outcomes in healthcare settings has not been systematically evaluated. OBJECTIVE: To identify and analyse the available evidence on the effect of shift length (8-h vs 12-h shifts) on quality of patient care and healthcare provider outcomes. METHODS: Systematic searching of eight online databases, key governmental/organisational websites and academic journals with ancestry search of relevant articles (limited to articles published in English and Spanish). RESULTS: Of 562 articles that were retrieved from 20 446 titles identified through database and manual searches, 27 satisfied the inclusion criteria, of which 15 were rejected because of low methodological quality. The 12 final studies included cross-sectional/survey (7), before-after (3) and prospective cohort (2) designs. The main primary outcomes evaluated were: (1) quality of patient care and (2) healthcare provider outcomes. The results were equivocal. With respect to the effect of shift length on quality of patient care, two studies found that errors and near errors were associated with working longer shifts, and another study reported decreased patient complications and length of stay with longer shifts. Specific healthcare provider outcomes such as health complaints, well-being, drug and alcohol consumption, stress and job satisfaction were mostly evaluated by single studies and therefore there was insufficient evidence from which to draw conclusions. CONCLUSIONS: Methodological quality of the studies generally was low and results equivocal with insufficient evidence to determine the effects of shift length on quality of patient care and healthcare provider outcomes. Clearly, robust well-designed studies are needed to examine the effect of shift length on patient and healthcare provider outcomes.
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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 | 0.014 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".