Evidence map›Paper›PMID 32625130›Full record

ArticleFrontiers in psychology2020

Development of a Computerized Adaptive Test for Separation Anxiety Disorder Among Adolescents.

Yiyuan Hu, Yan Cai, Dongbo Tu, Yingying Guo, Siyang Liu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in psychology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.1field-weighted citation impact, top 42% of its field
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

0 citing papers in PubMed, 1 citations in OpenAlex.

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 at 1 institution in 1 country.

Yiyuan HuSchool of Psychology, Jiangxi Normal University, Nanchang, China.
Yan CaiSchool of Psychology, Jiangxi Normal University, Nanchang, China.
Dongbo TuSchool of Psychology, Jiangxi Normal University, Nanchang, China.
Yingying GuoSchool of Psychology, Jiangxi Normal University, Nanchang, China.
Siyang LiuSchool of Psychology, Jiangxi Normal University, Nanchang, China.
Jiangxi Normal University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSeparation anxiety disorder (SAD) is one of the most common mental disorders among children and adolescents, and it may seriously affect their growth, daily life, and learning. Self-report scales have been used for diagnosis, which require lengthy testing and personnel.

methodsA total of 1,241 adolescents were recruited from 16 junior- and senior-high schools in China. The initial item bank was selected from classical SAD scales according to the

resultsAn average of 17 items per participant was required to achieve and maintain a 0.3 standard error of measurement in the SAD severity estimate. The estimated correlation of the CAT-SAD with the total 68-item test score was 0.955. CAT-SAD scores were strongly related to the probability of a SAD diagnosis with the Separation Anxiety Assessment Scale-Child and Adolescent Version. Therefore, SAD could be accurately predicted by the CAT-SAD.

conclusionsExploratory factor analyses revealed that SAD was unidimensional. The CAT-SAD, which has good reliability and validity and high sensitivity and specificity, provides an efficient test for adolescents with SAD as compared to standard paper-and-pencil tests. It can be used to diagnose varying degrees of SAD quickly and reliably and ease the burden on adolescents. Potential applications for inexpensive, efficient, and accurate screening of SAD are discussed.

Indexed as

adolescentcomputerized adaptive testingDSM-5item response theoryseparation anxiety disorder

Identifiers

PMID32625130
PMCPMC7315771
OpenAlexW3040024073

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.