Evidence map›Paper›PMID 41755684›Full record

ArticlePsyCh journal2026

Computer-Assisted Performance-Based Assessment for Mental Health: A Scoping Review.

Hanya Li, Shuang Li

Abstract readScoping Review
In one paragraph

Article in PsyCh journal, 2026. 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
–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

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.

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

2 authors.

Hanya LiSchool of Educational Technology, Faculty of Education, Beijing Normal University, Beijing, People's Republic of China.ORCID https://orcid.org/0009-0000-8997-8192
Shuang LiSchool of Educational Technology, Faculty of Education, Beijing Normal University, Beijing, People's Republic of China.

Funding

National Key Research and Development Program of China 2021YFC3340800
6 · The paper itself

Abstract

Adolescent mental health is foundational to personal development, yet it faces escalating challenges globally. While traditional assessment methods lack objectivity and ecological validity, integrating computer-assisted technology (CAT) into performance-based assessments (PBAs) offers a promising pathway. This review, following the PRISMA-ScR reporting standard, analyzed 89 articles (2015-2025) to map the assessed components, CAT applications, and scenario diversity in mental health PBAs. Analysis revealed a research emphasis on mental disorders, with critical domains for adolescent development remaining significantly understudied. CATs significantly enhanced PBAs through data analysis, data acquisition, scenario creation, and tool digitization. PBA scenarios are diverse, demonstrating the adaptability of PBAs for multidimensional mental health assessment. Prioritizing the design of PBAs for social-emotional and adaptive assessment is critical for the early identification of adolescent mental health issues. Furthermore, advancing predictive analytics and leveraging large language models for feedback generation are promising ways to unlock CAT's potential in enhancing PBAs. Importantly, integrating and adapting scenarios from validated scales by CATs into PBAs could further enhance assessment typicality and reliability.

Indexed as

Diagnosis, Computer-AssistedMental DisordersMental HealthAdolescentHumansassessment scenariocomputer‐assisted technologymachine learningmental healthperformance‐based assessment

Identifiers

PMID41755684
PMCPMC12946823

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.