Evidence map›Paper›PMID 42534001›Full record

ArticleJournal of military, veteran and family health2024

An application of the biopsychosocial model for pain in Canadian Veterans Life After Service Studies 2019 survey.

Julián Reyes-Vélez, Erin Buchanan, Jeffrey Pavlacic, Jill Sweet, Lisa Garland Baird

Abstract read
In one paragraph

Article in Journal of military, veteran and family health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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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

5 authors.

Julián Reyes-VélezResearch Directorate, Veterans Affairs Canada, Charlottetown, Prince Edward Island, Canada.
Erin BuchananCognitive Analytics, Harrisburg University of Science and Technology, Harrisburg, Pennsylvania, United States.
Jeffrey PavlacicDepartment of Psychiatry & Behavioral Sciences, Medical University of South Carolina, Charleston, South Carolina, United States.
Jill SweetResearch Directorate, Veterans Affairs Canada, Charlottetown, Prince Edward Island, Canada.
Lisa Garland BairdResearch Directorate, Veterans Affairs Canada, Charlottetown, Prince Edward Island, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Chronic pain is highly prevalent and disruptive to the general population. Relative to civilians, Veterans are more likely to experience chronic pain due to occupational requirements during military service and associated psychological difficulties. The biopsychosocial model is a promising theoretical framework for understanding potential causes of chronic pain, as well as a path for implementing evidence-based interventions. Methods: This study applied the biopsychosocial framework to data from the Life After Service Studies 2019 survey by modelling biopsychosocial predictors of chronic pain and controlling for relevant participant and military demographics. The use of structural equation modelling solutions allowed the biopsychosocial model to be assessed simultaneously as a model with latent variables, rather than traditional regression or logistic analyses. Results: Biological factors and a combined psychological and social factor were related to an increased likelihood of having chronic pain. The three-model solution showed anxiety, posttraumatic stress disorder, suicidal ideation, and life stress were positively associated, while hypervigilance, alcohol use, social support, and labour force status was negatively related to the latent variable. Discussion: Results showed specific biopsychosocial factors were related to chronic pain through different structural equation modelling solutions. Understanding the contribution of biopsychosocial factors can inform evidence-based interventions and policy changes through multidisciplinary approaches.

Indexed as

biopsychosocialCanadachronic painepidemiologyLASSLife After Service Studiesmilitarystructural equation modellingVeterans

Identifiers

PMID42534001
PMCPMC13293218

What OpenQuestion holds

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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.