Evidence map›Paper›PMID 37533105›Full record

ArticleBMC psychology2023

Rasch analysis of the Chinese version of the clinically useful depression outcome scale in patients with major depressive disorder.

Jing Zhong, Hai-Yan Ma, Xue-Mei Wang, Xiao-Jie Huang, Ming-Zhi Xu

Abstract read
In one paragraph

Article in BMC psychology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

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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

1 citing paper in PubMed.

  1. Review
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.

Jing ZhongSchool of Public Health, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.
Hai-Yan MaGuangdong second provincial general hospital, Guangzhou, Guangdong, People's Republic of China.
Xue-Mei WangThe Second Clinical College, Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, People's Republic of China.
Xiao-Jie HuangGuangdong Mental Health Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510120, Guangdong, People's Republic of China.
Ming-Zhi XuSchool of Public Health, Southern Medical University, Guangzhou, Guangdong, People's Republic of China. xumingzhi@gdph.org.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo examine the psychometric properties of the Chinese version of the Clinically Useful Depression Outcome Scale (CUDOS) in the Chinese patients with major depressive disorder (MDD) using Rasch analysis.

methodsThe sample consisted of 283 patients with MDD (69% females). The Rasch model was applied to examine the overall fit of the Chinese version of CUDOS and the fit of the 18 items. Dimensionality, item-model fit, differential item functioning (DIF), reliability, ordering of response category and targeting were tested to examine the psychometric properties of the Chinese version of CUDOS.

resultsRasch analysis demonstrated the unidimensionality of the Chinese version of CUDOS. Of the 18 items, three items (item 4, item 5, item 6) showed misfit in the model. After merging item 4 into item 3 and item 6 into item 5, the overall model fit improved. The person separation index (PSI) was 3.0 and the person reliability coefficient was 0.90. No evidence of significant DIF was found when associated with gender and age. No disordered category and threshold of the rating response were observed, which meant the response category setting was reasonable. The mean ability of person was - 0.53.

conclusionThe results suggested that the Chinese version of CUDOS has acceptable psychometric properties. In order to improve the quality and applicability of the Chinese version of CUDOS, the merging of item 4 into item 3 and item 6 into item 5 are suggested.

Indexed as

Major Depressive DisorderDepressionFemaleHumansMalePsychometricsReproducibility of ResultsSurveys and QuestionnairesClinically useful Depression Outcome Scale (CUDOS)DepressionPsychometric propertiesRasch analysis

Identifiers

PMID37533105
PMCPMC10398914

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