Evidence map›Paper›PMID 38667814›Full record

ArticleEuropean journal of investigation in health, psychology and education2024

An Interpretable Machine Learning Approach to Predict Sensory Processing Sensitivity Trait in Nursing Students.

Alicia Ponce-Valencia, Diana Jiménez-Rodríguez, Juan José Hernández Morante, Carlos Martínez Cortés, Horacio Pérez-Sánchez, Paloma Echevarría Pérez

Open access · goldAbstract read
In one paragraph

Article in European journal of investigation in health, psychology and education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.2field-weighted citation impact, top 21% 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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
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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

6 authors at 2 institutions in 1 country.

Alicia Ponce-ValenciaFaculty of Nursing, Universidad Católica de Murcia, Campus de Guadalupe, 30107 Murcia, Spain.ORCID 0000-0003-1238-9423
Diana Jiménez-RodríguezFaculty of Health Sciences, Universidad de Almería, Carretera Sacramento s/n, 04120 Almería, Spain.ORCID 0000-0002-1169-0428
Juan José Hernández MoranteFaculty of Nursing, Universidad Católica de Murcia, Campus de Guadalupe, 30107 Murcia, Spain.ORCID 0000-0001-9255-4630
Carlos Martínez CortésStructural Bioinformatics and High-Performance Computing (BIO-HPC) Research Group, Universidad Católica de Murcia, Campus de Guadalupe, 30107 Murcia, Spain.
Horacio Pérez-SánchezStructural Bioinformatics and High-Performance Computing (BIO-HPC) Research Group, Universidad Católica de Murcia, Campus de Guadalupe, 30107 Murcia, Spain.ORCID 0000-0003-4468-7898
Paloma Echevarría PérezFaculty of Nursing, Universidad Católica de Murcia, Campus de Guadalupe, 30107 Murcia, Spain.ORCID 0000-0002-2458-611X
Universidad Católica de Murcia · ESUniversity of Almería · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sensory processing sensitivity (SPS) is a personality trait that makes certain individuals excessively sensitive to stimuli. People carrying this trait are defined as Highly Sensitive People (HSP). The SPS trait is notably prevalent among nursing students and nurse staff. Although there are HSP diagnostic tools, there is little information about early detection. Therefore, the aim of this work was to develop a prediction model to identify HSP and provide an individualized nursing assessment. A total of 672 nursing students completed all the evaluations. In addition to the HSP diagnosis, emotional intelligence, communication skills, and conflict styles were evaluated. An interpretable machine learning model was trained to predict the SPS trait. We observed a 33% prevalence of HSP, which was higher in women and people with previous health training. HSP were characterized by greater emotional repair (

Indexed as

communication skillsconflict resolutionemotional intelligenceinterpretable machine learningnursingsensory processing sensitivity

Identifiers

PMID38667814
PMCPMC11049261
OpenAlexW4393860038

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.