Evidence map›Paper›PMID 37993675›Full record

ArticleJournal of occupational rehabilitation2024

Trajectories of Self-Rated Health Among Industrially Disabled Individuals: A Latent Class Growth Analysis.

Sujin Lee, Han Nah Park, Ju Young Yoon

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Article in Journal of occupational rehabilitation, 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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5 · Who and what money

Authors and funding

3 authors.

Sujin LeeCollege of Nursing, Seoul National University, Seoul, Republic of Korea.ORCID 0000-0002-8935-669X
Han Nah ParkCollege of Nursing, Seoul National University, Seoul, Republic of Korea.ORCID 0000-0003-2685-5747
Ju Young YoonCollege of Nursing, Seoul National University, Seoul, Republic of Korea. yoon26@snu.ac.kr.ORCID 0000-0003-3944-0663

Funding

Ministry of Education - Korea, Republic of 2022R1I1A2072907
6 · The paper itself

Abstract

backgroundUnderstanding the self-rated health of industrially disabled individuals is an important variable that significantly affects their quality of life, satisfaction, and return to work after an industrial accident. Since the health of people with industrial disabilities is affected by various environments and variables, interventions and policies that are suitable for their characteristics are needed.

objectivesThis study aimed to identify changes in self-rated health among industrially disabled individuals, distinguish between different latent classes, and verify predictive factors for each latent class.

methodsFour time-point datasets from the 2018-2021 panel study of Korean workers' compensation insurance were used. Using the latent growth curve model, an overall trajectory of self-rated health of industrially disabled individuals was confirmed, and the number and characteristics of different trajectories were identified using the latent class growth model. Multinomial logistic regression analysis was used to identify the predictive factors for each class.

resultsFour classes of self-rated health trajectories were identified: low-decreasing (21.7%), moderate-increasing (15.7%), high-decreasing (56.1%), and low-stable (6.5%) classes. A multinomial logistic regression analysis revealed that significant determinants (age, capacity, type of industrial accident, grade of disability, mental activity, outdoor activity, and social relationships) were different for each latent class. Capacity level affected all potential class classifications.

conclusionsTo improve the self-rated health of industrially disabled individuals, it is necessary to develop an appropriate strategy that considers the characteristics of the latent class.

Indexed as

Latent Class AnalysisPersons with DisabilitiesAdultDiagnostic Self EvaluationFemaleHealth StatusHumansMaleMiddle AgedRepublic of KoreaSelf ReportWorkers' CompensationIndustrial disabledLatent class growth modelingLatent growth curve modelingSelf-rated health

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