Evidence map›Paper›PMID 40724063›Full record

ArticleInternational journal of environmental research and public health2025

Using Machine Learning to Predict Resilience Among Nurses in a South African Setting.

Jennifer Chipps, Amanda Cromhout, Umit Tokac

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. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

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.

Jennifer ChippsSchool of Nursing, Faculty of Community Health Sciences, University of Western Cape, Cape Town 7441, South Africa.ORCID 0000-0002-7895-4483
Amanda CromhoutSchool of Nursing, Faculty of Community Health Sciences, University of Western Cape, Cape Town 7441, South Africa.ORCID 0000-0002-0008-8212
Umit TokacCollege of Nursing, University of Missouri, St. Louis, MO 63121, USA.ORCID 0000-0001-6698-1401

Funding

South African MRC Self Initiated Funding Not stated
6 · The paper itself

Abstract

Nursing is a stressful profession. Stress can affect the mental health of nurses. A positive response to stress, resilience, is known to be a protective factor against mental health issues. This study aimed to use machine learning with secondary data from five survey studies, conducted between 2022 and 2023, to identify factors predicting high versus low levels of resilience in South African nursing samples from the Western Cape Province, South Africa. The sample included (1134 records (male = 250, 22.0%, female = 874, 77.1%, and other = 10 (0.9%) included all data on all categories of nursing staff (student nurses (567, 50%), professional registered nurses (315, 27.8%), and non-professional nurses (246, 21.7%) who completed a survey using a response to stress scale. We used random forest analysis, demographic variables, years of experience, and a brief 4-item screen of resilience to predict resilience. The model yielded limited added value from demographic groupings in this model, but the brief screening had an overall classification accuracy of 86.41% (95% CI: 0.810; 0.908).

Indexed as

Machine LearningNursesResilience, PsychologicalAdultFemaleHumansMaleMiddle AgedSouth AfricaSurveys and QuestionnairesYoung Adultmachine learningmental healthnursesrandom forestresilience

Identifiers

PMID40724063
PMCPMC12294786

What OpenQuestion holds

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LicenceCC BY
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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.