Evidence map›Paper›PMID 39095975›Full record

ArticleBritish journal of psychology (London, England : 1953)2026

Scoping review on natural language processing applications in counselling and psychotherapy.

Maria Laricheva, Yan Liu, Edward Shi, Amery Wu

Abstract readScoping Review
In one paragraph

Article in British journal of psychology (London, England : 1953), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. How AI can advance psychological science.British journal of psychology (London, England : 1953) · 2026
    Article
  4. Article
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

4 authors.

Maria LarichevaEducational and Counselling Psychology, and Special Education, The University of British Columbia, Vancouver, British Columbia, Canada.ORCID https://orcid.org/0000-0003-0369-387X
Yan LiuPsychology, Carleton University, Ottawa, Ontario, Canada.
Edward ShiArts, Business and Law, Victoria University Melbourne, Melbourne, Victoria, Australia.
Amery WuEducational and Counselling Psychology, and Special Education, The University of British Columbia, Vancouver, British Columbia, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent years have witnessed some rapid and tremendous progress in natural language processing (NLP) techniques that are used to analyse text data. This study endeavours to offer an up-to-date review of NLP applications by examining their use in counselling and psychotherapy from 1990 to 2021. The purpose of this scoping review is to identify trends, advancements, challenges and limitations of these applications. Among the 41 papers included in this review, 4 primary study purposes were identified: (1) developing automated coding; (2) predicting outcomes; (3) monitoring counselling sessions; and (4) investigating language patterns. Our findings showed a growing trend in the number of papers utilizing advanced machine learning methods, particularly neural networks. Unfortunately, only a third of the articles addressed the issues of bias and generalizability. Our findings provided a timely systematic update, shedding light on concerns related to bias, generalizability and validity in the context of NLP applications in counselling and psychotherapy.

Indexed as

CounselingNatural Language ProcessingPsychotherapyHumansMachine Learningbiascounsellinggeneralizabilitynatural language processingpsychotherapyscoping review

Identifiers

PMID39095975
PMCPMC13051009

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.