Evidence map›Paper›PMID 34682482›Full record

Observational studyInternational journal of environmental research and public health2021

Exploring the Environment behind In-Patient Falls and Their Relation to Hospital Overcrowdedness-A Register-Based Observational Study.

Dimitrios Stathopoulos, Eva Ekvall Hansson, Kjerstin Stigmar

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.7field-weighted citation impact, top 27% of its field
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

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

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

3 authors at 1 institution in 1 country.

Dimitrios StathopoulosDepartment of Health Sciences, Lund University, 22100 Lund, Sweden.ORCID 0000-0002-9458-9324
Eva Ekvall HanssonDepartment of Health Sciences, Lund University, 22100 Lund, Sweden.ORCID 0000-0001-7552-6486
Kjerstin StigmarDepartment of Health Sciences, Lund University, 22100 Lund, Sweden.
Lund University · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

(1) Background: Inpatient falls are a serious threat to patients' safety and their extrinsic factors are, at present, insufficiently described. Additionally, hospital overcrowdedness is known for its malicious effects but its relation to the inpatient falls is currently underexplored. The aim of this study was to explore the distribution of falls and their extrinsic characteristics amongst a range of different clinics, and to explore the correlation and predictive ability of hospital overcrowding in relation to inpatient falls. (2) Methods: An observational, cross-sectional, registry-based study was conducted using retrospective data from an incidence registry of a hospital organization in Sweden during 2018. The registry provided data regarding the extrinsic factors of inpatient falls, including the clinics' overcrowdedness. Simple descriptive statistics, correlation analysis and simple linear regression analysis were used. (3) Results: Twelve clinics were included. A total of 870 inpatient falls were registered during 2018. Overcrowdedness and total amount of falls were positively and very strongly correlated (r = 0.875,

Indexed as

Accidental FallsInpatientsCross-Sectional StudiesHospitalsHumansRetrospective StudiesRisk Factorsextrinsic factorsfallshospital overcrowdednessimpatient fallsorganizational factors

Identifiers

PMID34682482
PMCPMC8535502
OpenAlexW3206403775

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.