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Association of non-standard working time arrangements with safety incidents: a systematic review

Por: Moen · L. V. · S Lie · J.-A. · Sterud · T. · Christensen · J. O. · Haugen · F. · Skogstad · M. · Nordby · K.-C. · Matre · D.
Objective

To systematically review the evidence on the association between non-standard working time arrangements (such as night work or shift work) and the occurrence of safety incidents.

Design

Systematic review conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and using a structured narrative approach and the Synthesis Without Meta-analysis framework to evaluate and summarise findings.

Data sources

MEDLINE, Embase, PsycINFO, Web of Science and ProQuest Health and Safety Science Abstracts were searched through February 2024.

Eligibility criteria for selecting studies

We included peer-reviewed English-language studies of paid workers (18–70 years) that examined the association between non-standard working time arrangements and safety incidents (accidents, near-accidents, safety incidents or injuries), excluding cross-sectional designs and studies on unpaid workers, athletes or military personnel.

Data extraction and synthesis

Two reviewers independently extracted data and assessed risk of bias using standardised forms, extracting study characteristics (author, year, country, sector and population), working time arrangements and exposure assessment, outcomes and their assessment, and reported risk estimates. We conducted a narrative synthesis, classifying studies into three exposure contrasts (shift worker versus non-shift worker, time-of-day and shift intensity), and summarised risk estimates using forest plots without calculating pooled effects.

Results

A total of 13 569 records were screened, and 24 studies met the inclusion criteria. The results indicated that shift workers generally had an elevated safety incident risk compared with non-shift workers (risk estimates ranged from 1.11 to 5.33). Most of the included studies found an increased risk of safety incidents during or after night shifts. Accumulated exposure to evening or night shifts increased the risk of safety incidents during the following 7 days. However, bias and heterogeneity across studies in design, populations and outcome measures resulted in an overall low to very low certainty of the evidence.

Conclusions

Non-standard working time arrangements, including night and evening shifts, appear to increase the risk of occupational safety incidents. Despite the low certainty of evidence, the findings highlight a potential area for preventive measures in work scheduling. Future longitudinal studies using individual data on daily working hours are needed.

Exploring the Documentation of Delirium in Patients After Cardiac Surgery: A Retrospective Patient Record Study

imageDelirium is a common disorder for patients after cardiac surgery. Its manifestation and care can be examined through EHRs. The aim of this retrospective, comparative, and descriptive patient record study was to describe the documentation of delirium symptoms in the EHRs of patients who have undergone cardiac surgery and to explore how the documentation evolved between two periods (2005-2009 and 2015-2020). Randomly selected care episodes were annotated with a template, including delirium symptoms, treatment methods, and adverse events. The patients were then manually classified into two groups: nondelirious (n = 257) and possibly delirious (n = 172). The data were analyzed quantitatively and descriptively. According to the data, the documentation of symptoms such as disorientation, memory problems, motoric behavior, and disorganized thinking improved between periods. Yet, the key symptoms of delirium, inattention, and awareness were seldom documented. The professionals did not systematically document the possibility of delirium. Particularly, the way nurses recorded structural information did not facilitate an overall understanding of a patient's condition with respect to delirium. Information about delirium or proposed care was seldom documented in the discharge summaries. Advanced machine learning techniques can augment instruments that facilitate early detection, care planning, and transferring information to follow-up care.
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