ArticleClinical psychological science : a journal of the Association for Psychological Science2026
Applying Artificial Intelligence to Expand the Measurement Tool Kit in Clinical-Psychological Science: Moving Beyond Self-Reports.
Article in Clinical psychological science : a journal of the Association for Psychological Science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Objective Assessment in Clinical Psychological Science: Progress in Wearable Alcohol Biosensors.Annual review of clinical psychology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Research exploring correlates of, precursors to, and consequences of psychological disorders has often relied on designs wherein both predictor and outcome are measured by self-reports. In this article, coauthored by a clinical psychologist (C. E. Fairbairn) and a data scientist (N. Bosch), we offer information surrounding an evolving class of machine-learning models as these inform an expanding measurement tool kit in clinical-psychological science. Specifically, we note the development of deep-learning applications for image analysis, language analysis, and the analysis of physiological time-series data, reviewing implications of these advances for measurement in behavioral research. We weigh strengths and limitations of these automated methods in comparison with self-reports, including the specific form of error likely yielded via each (random vs. systematic), with the aim of fostering a replicable, sustainable, and reputationally strong field of clinical-psychological science.
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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.