Evidence map›Paper›PMID 41302682›Full record

ArticleInternational journal of environmental research and public health2025

Transition from 24-Hour Shifts to Safer Work Schedules for Nurses in Latvian Healthcare: Policy Analysis and Recommendations.

Olga Cerela-Boltunova, Kristine Klavina

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Olga Cerela-BoltunovaDepartment of Nursing and Midwifery, Riga Stradiņš University, LV-1067 Rīga, Latvia.ORCID 0000-0001-8258-2005
Kristine KlavinaDevelopment of Human Resources Sector, Ministry of Health the Republic of Latvia, LV-1067 Rīga, Latvia.

Funding

Riga Stradiņš University
6 · The paper itself

Abstract

In Latvia, extended shifts, including 24 h duties, remain common inpatient care settings despite extensive international evidence on their adverse effects on staff well-being and patient safety. We conducted an evidence-informed policy analysis combining a structured review of national legislation and institutional reports with comparative policy mapping across OECD/EU countries. The interpretation was guided by three theoretical frameworks: the Job Demands-Resources model, Effort-Recovery theory, and the Work-Life Interface framework. Latvian practice shows high reliance on long shifts amid workforce shortages and incomplete overtime/rest accounting. In contrast, most OECD and EU countries have implemented 8-12 h multi-shift systems with mandated rest, which are associated with lower error rates, reduced burnout, and higher staff satisfaction. We synthesised four policy options (12 h transition model; 16 h cap; modular 2 × 6 h/3 × 8 h; flexible unit-profiled schedules) and identify seven prerequisites for feasible implementation (regulatory alignment; staffing; financing; management training; digital scheduling; pilot projects; monitoring). A phased transition from 24 h shifts to structured schedules appears both feasible and desirable, with pilot implementation and monitoring aligning with WHO/ILO recommendations. Implications for policy and practice: Reform is a system-level intervention to improve staff well-being, patient safety, and workforce sustainability in Latvia.

Indexed as

Health PolicyNursesPersonnel Staffing and SchedulingWork Schedule ToleranceHumansLatviaPolicy Makinghealth policyhealth workforceLatvianursing staffwork schedule tolerance

Identifiers

PMID41302682
PMCPMC12652771

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.