Evidence map›Paper›PMID 32326220›Full record

ArticleInternational journal of environmental research and public health2020

Association Rule Learning Is an Easy and Efficient Method for Identifying Profiles of Traumas and Stressors that Predict Psychopathology in Disaster Survivors: The Example of Sri Lanka.

Nuwan Jayawickreme, Ehsan Atefi, Eranda Jayawickreme, Jiale Qin, Amir H Gandomi

Open access · goldAbstract read
In one paragraph

Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 15% 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.

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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

5 authors at 4 institutions in 2 countries.

Nuwan JayawickremeDepartment of Psychology, Manhattan College, Bronx, NY 18966, USA.ORCID 0000-0002-6001-8996
Ehsan AtefiDepartment of Mechanical Engineering, Manhattan College, Bronx, NY 18966, USA.
Eranda JayawickremeDepartment of Psychology, Wake Forest University, Winston-Salem, NC 27109, USA.
Jiale QinSchool of Business, Stevens Institute of Technology, Hoboken, NJ 07030, USA.
Amir H GandomiFaculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia.
Manhattan University · USStevens Institute of Technology · USUniversity of Technology Sydney · AUWake Forest University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Research indicates that psychopathology in disaster survivors is a function of both experienced trauma and stressful life events. However, such studies are of limited utility to practitioners who are about to go into a new post-disaster setting as (1) most of them do not indicate which specific traumas and stressors are especially likely to lead to psychopathology; and (2) each disaster is characterized by its own unique traumas and stressors, which means that practitioners have to first collect their own data on common traumas, stressors and symptoms of psychopathology prior to planning any interventions. An easy-to-use and easy-to-interpret data analytical method that allows one to identify profiles of trauma and stressors that predict psychopathology would be of great utility to practitioners working in post-disaster contexts. We propose that association rule learning (ARL), a big data mining technique, is such a method. We demonstrate the technique by applying it to data from 337 survivors of the Sri Lankan civil war who completed the Penn/RESIST/Peradeniya War Problems Questionnaire (PRPWPQ), a comprehensive, culturally-valid measure of experienced trauma, stressful life events, anxiety and depression. ARL analysis revealed five profiles of traumas and stressors that predicted the presence of some anxiety, three profiles that predicted the presence of severe anxiety, four profiles that predicted the presence of some depression and five profiles that predicted the presence of severe depression. ARL allows one to identify context-specific associations between specific traumas, stressors and psychological distress, and can be of great utility to practitioners who wish to efficiently analyze data that they have collected, understand the output of that analysis, and use it to provide psychosocial aid to those who most need it in post-disaster settings.

Indexed as

DisastersMachine LearningMental DisordersStress Disorders, Post-TraumaticSurvivorsHumansSri Lankaassociation rule learningdaily stressorsdisasterdisaster survivorsmachine learningSri Lankatraumawarwar survivorswar trauma

Identifiers

PMID32326220
PMCPMC7215723
OpenAlexW3019183519

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.