Evidence map›Paper›PMID 40537737›Full record

ArticleBMC public health2025

Mental health in children with and without disabilities in a register-based Swedish sample supports the two-continua model: a latent class analysis.

Lina Homman, Lilly Augustine, Mats Granlund

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Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

What it found

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Lina HommanDisability research division, Institution of behaviour and learning, Linköping university, Linköping, Sweden. lina.homman@liu.se.
Lilly AugustineCHILD, School of Education and Communication, Jönköping university, Jönköping, Sweden.
Mats GranlundCHILD, School of health and welfare, Jönköping university, Jönköping, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMental health is a term frequently used to describe mental health problems. However, mental health includes both mental health problems and well-being. Therefore, mental health can be seen as having two distinct yet related dimensions, as described in the two-continua model of mental health (Westerhof & Keyes, 2010) where an individual can simultaneously experience any combination of well-being and problems, suggesting four classes: (i) high well-being, low problems; (ii) high well-being, high problems; (iii) low well-being, low problems; and (iv) low well-being, high problems. Through this framework an understanding of differences in putative risk and protective factors can be gained when compared across classes. While the model has received support, it is unclear how it applies to children. In particular, children with disabilities, as disabilities pose a risk factor to poor mental health. A greater understanding of similarities and differences between children with and without disabilities, and of risk and protective factors, could help tailor support focused on enhancing well-being, both as a goal and as a means to better self-management of mental health.

methodsThe present project utilizes Sweden Statistics (SCB) study (barnULF) to measure life conditions. Nearly 4000 children aged 10-18, with and without disabilities, and their caregivers (ULF/SILC) were studied through yearly interview-based sample surveys conducted between 2013 and 2019. Latent class analysis was performed to assess whether the data fit a 4-class model in line with the two-continua model. Possible factors influencing mental health, including participation, were compared across the identified classes and between children with and without disabilities.

resultsThe analysis confirmed the predicted model. Each class showed distinct features regarding putative risk and protective factors of mental health and demographics in both the child and caregiver. These features differed significantly between children with and without disabilities, especially relating to participation, social bonds, family functioning, digital media use, and perceived safety. Age, disability, and gender predicted class adversity.

conclusionsThe results suggest that mental health problems and well-being are two related but separate constructs, highlighting the importance of promoting participation and recognizing well-being and not just mental health problems when planning interventions. The results also highlight the importance of providing support for not only the child but also their caregiver.

Indexed as

Children with DisabilitiesMental DisordersMental HealthAdolescentChildFemaleHumansLatent Class AnalysisMaleRegistriesRisk FactorsSwedenChildrenDisabilitiesLatent class analysisMental healthTwo-continua model

Identifiers

PMID40537737
PMCPMC12180166

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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.