Measuring Positive Mental Health and Depression in Africa: A Variable-Based and Person-Centred Analysis of the Dual-Continua Model.

Measuring Positive Mental Health and Depression in Africa: A Variable-Based and Person-Centred Analysis of the Dual-Continua Model.

Khumalo, Itumeleng P;Appiah, Richard;Wilson Fadiji, Angelina;
Frontiers in psychology 2022 Vol. 13 pp. 885278
58
khumalo2022measuringfrontiers

Abstract

The dual-continua model of mental health provides a contemporary framework for conceptualising and operationalising mental health. According to this model, mental health is distinct from but related to mental illness, and not the opposite or merely the absence of psychopathology symptoms. To examine the validity of the dual-continua model, previous studies have either applied variable-based analysis such as confirmatory factor analysis (CFA), or used predetermined cut-off points for subgroup division. The present study extends this contribution by subjecting data from an African sample to both CFA and latent class analysis (LCA) to test the dual-continua model in Africa. We applied CFA separately for the Mental Health Continuum-Short Form (MHC-SF) and Patient Health Questionnaire-9 (PHQ-9); and LCA on combined item responses. College students ( = 892; average age = 22.74, = 4.92; female = 58%) from Ghana ( = 309), Kenya ( = 262), Mozambique ( = 232), and South Africa ( = 89) completed the MHC-SF and PHQ-9. With minor modifications to the measurement models, the CFA results of this study confirm the three-factor structure of the MHC-SF, and a unidimensional solution for the PHQ-9. LCA results show the presence of three distinct latent classes: languishing with moderate endorsement of depressive symptoms (25.9%), flourishing with low endorsement of depressive symptoms (63.7%), and moderate mental health with high endorsement of depressive symptoms (10.4%). These findings further contribute to affirming the evidence for the dual-continua model of mental health, with implications for the assessment of mental health, to inform policy, practise, and future research in community and clinical settings in Africa.

Access

Citation

ID: 276711
Ref Key: khumalo2022measuringfrontiers
Use this key to autocite in SciMatic or Thesis Manager

References

Blockchain Verification

Account:
NFT Contract Address:
0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
276711
Unique Identifier:
885278
Network:
Scimatic Chain (ID: 481)
Loading...
Blockchain Readiness Checklist
Authors
Abstract
Journal Name
Year
Title
5/5
Creates 1,000,000 NFT tokens for this article
Token Features:
  • ERC-1155 Standard NFT
  • 1 Million Supply per Article
  • Transferable via MetaMask
  • Permanent Blockchain Record
Blockchain QR Code
Scan with Saymatik Web3.0 Wallet

Saymatik Web3.0 Wallet