Evidence map›Paper›PMID 40247085›Full record

ArticleScientific reports2025

Impact of COVID-19 on nurse staffing levels and healthcare-associated infections in medical institutions: a retrospective cohort study.

Kyu-Tae Han, Seungju Kim

Abstract read
In one paragraph

Article in Scientific reports, 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.

Kyu-Tae HanDivision of Cancer Control & Policy, National Cancer Control Institute, National Cancer Center, Goyang, Republic of Korea.
Seungju KimDepartment of Health System, College of Nursing, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea. seungju.phd@gmail.com.

Funding

National Research Foundation of Korea NRF-2022R1C1C1002766
6 · The paper itself

Abstract

The COVID-19 pandemic changed the healthcare environment and raised concerns about the impact on nurse staffing and healthcare-associated infections (HAIs), but evidence on this impact is lacking. This study evaluated changes in nurse staffing levels in South Korea and their impact on healthcare-associated COVID-19 infection (HA COVID-19). A total of 67,154 cases were included, and survival analysis was performed using a Cox proportional hazards model to assess the association between nurse staffing and HA COVID-19. Among patients admitted to intensive care units and general wards, 11.7% and 15.1%, respectively, contracted COVID-19 in the hospital after admission. Survival analysis showed that HA COVID-19 increased as the nurse-to-patient ratio increased in general wards. Additionally, compared with medical institutions that do not operate fixed night shift nurses, HA COVID-19 increased as the proportion of night nurses increased, but it decreased to less than 10%. This study shows that securing nurses and operating an appropriate proportion of night nursing staff is important for infection control. To prepare for the spread of infectious diseases and implement infection prevention activities, it is necessary to secure an appropriate level of nursing staff; maintaining an appropriate proportion of night shift nurses will help reduce HAIs.

Indexed as

COVID-19Cross InfectionNursing Staff, HospitalPersonnel Staffing and SchedulingAdultAgedFemaleHumansInfection ControlIntensive Care UnitsMaleMiddle AgedRepublic of KoreaRetrospective StudiesSARS-CoV-2General wardHealthcare-associated infectionIntensive care unitNurse staff

Identifiers

PMID40247085
PMCPMC12006407

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.