ArticleFrontiers in psychology2021
Freely Generated Word Responses Analyzed With Artificial Intelligence Predict Self-Reported Symptoms of Depression, Anxiety, and Worry.
Article in Frontiers in psychology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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.
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.
Who cites it
13 citing papers in PubMed.
- Bridging psychometrics and language: a method for extracting leadership insights from open-text responses.Frontiers in psychology · 2026Article
- Advancing psychological assessment: quantifying self-compassion through free-text responses and language model BERT.Scientific reports · 2025Article
- Towards a latent space cartography of subjective experience in mental health.Psychiatry and clinical neurosciences · 2025Article
- Multiscale Contextual Mamba: Advancing Psychiatric Disorder Detection across Multisite Functional Magnetic Resonance Imaging Datasets via State Space Modeling.Health data science · 2025Article
- Statistics is not measurement: The inbuilt semantics of psychometric scales and language-based models obscures crucial epistemic differences.Frontiers in psychology · 2025Article
- Question-based computational language approach outperforms rating scales in quantifying emotional states.Communications psychology · 2024Article
- Beyond rating scales: With targeted evaluation, large language models are poised for psychological assessment.Psychiatry research · 2024Review
- Measuring the menu, not the food: "psychometric" data may instead measure "lingometrics" (and miss its greatest potential).Frontiers in psychology · 2024Review
- The text-package: An R-package for analyzing and visualizing human language using natural language processing and transformers.Psychological methods · 2023Article
- Assessment of depression and anxiety in young and old with a question-based computational language approach.Npj mental health research · 2023Article
- Precise language responses versus easy rating scales-Comparing respondents' views with clinicians' belief of the respondent's views.PloS one · 2023Article
- Natural language analyzed with AI-based transformers predict traditional subjective well-being measures approaching the theoretical upper limits in accuracy.Scientific reports · 2022Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundQuestion-based computational language assessments (QCLA) of mental health, based on self-reported and freely generated word responses and analyzed with artificial intelligence, is a potential complement to rating scales for identifying mental health issues. This study aimed to examine to what extent this method captures items related to the primary and secondary symptoms associated with Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) described in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). We investigated whether the word responses that participants generated contained information of all, or some, of the criteria that define MDD and GAD using symptom-based rating scales that are commonly used in clinical research and practices.
methodParticipants (
resultsThe QCLA correlated significantly with the individual items connected to the DSM-5 diagnostic criteria of MDD (PHQ-9; Pearson's
conclusionTogether these results suggest that QCLA may be able to complement rating scales in measuring mental health in clinical settings. The approach carries the potential to personalize assessments and contributes to the ongoing discussion regarding the diagnostic heterogeneity of depression.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.