CoChat
Sign Up
Cited by 141Psychotherapy Techniques and ApplicationsMental Health via WritingComputational and Text Analysis Methods

The DOI matches its Crossref record; 72 of the 73 references it cites were found in a registry or an index, and 1 are not checked yet.

Machine learning and natural language processing in psychotherapy research: Alliance as example use case.

Open access
Goldberg, Simon B., Flemotomos, Nikolaos, Martinez, Victor R., Tanana, Michael J., Kuo, Patty B., Pace, Brian T., Villatte, Jennifer L., Georgiou, Panayiotis G., Van Epps, Jake, Imel, Zac E., Narayanan, Shrikanth S., Atkins, David C.·2020·Journal of Counseling Psychology
DOI 10.1037/cou0000382PMC PMC7393999
Open this paper in CoChat — free account

Abstract

Artificial intelligence generally and machine learning specifically have become deeply woven into the lives and technologies of modern life. Machine learning is dramatically changing scientific research and industry and may also hold promise for addressing limitations encountered in mental health care and psychotherapy. The current paper introduces machine learning and natural language processing as related methodologies that may prove valuable for automating the assessment of meaningful aspects of treatment. Prediction of therapeutic alliance from session recordings is used as a case in point. Recordings from 1,235 sessions of 386 clients seen by 40 therapists at a university counseling center were processed using automatic speech recognition software. Machine learning algorithms learned associations between client ratings of therapeutic alliance exclusively from session linguistic content. Using a portion of the data to train the model, machine learning algorithms modestly predicted alliance ratings from session content in an independent test set (Spearman's ρ = .15, p < .001). These results highlight the potential to harness natural language processing and machine learning to predict a key psychotherapy process variable that is relatively distal from linguistic content. Six practical suggestions for conducting psychotherapy research using machine learning are presented along with several directions for future research. Questions of dissemination and implementation may be particularly important to explore as machine learning improves in its ability to automate assessment of psychotherapy process and outcome. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1037/cou0000382

Cites

72 of 73 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 72 references

Research works printed but not linked: 11

  1. Baldwin SA, & Imel ZE (2013). Therapist effects: Findings and methods In Lambert MJ (Ed.), Bergin and Garfield’s Handbook of Psychotherapy and Behavior Change (6th ed.) (p. 258– 297). Hoboken, NJ: Wiley & Sons.
  2. Cristianini N & Shawe-Taylor J (2000). An introduction to Support Vector Machines and other kernel-based learning methods. Cambridge: Cambridge University Press.
  3. Duncan BL, Miller SD, Sparks JA, Claud DA, Reynolds LR,…& Johnson LD (2003). The Session Rating Scale: Preliminary psychometric properties of a “working” alliance measure. Journal of Brief Therapy, 3(1), 3– 12.
  4. Dyson FJ (1998). Imagined worlds (Vol. 6). Cambridge, MA: Harvard University Press.
  5. Jurafsky D, & Martin JH (2014). Speech and language processing (2nd Ed.). London: Pearson.
  6. Mikolov T, Sutskever I, Chen K, Corrado GS, & Dean J (2013). Distributedrepresentations of words and phrases and their compositionality. In Advances in neural information processing systems (pp. 3111– 3119).
  7. Mitchell TM (1997). Does machine learning really work? AI Magazine, 18(3), 11– 20.
  8. Pagliardini M, Gupta P, & Jaggi M (2018). Unsupervised learning of sentence embeddingsusing compositional n-gram features. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (Vol. 1, pp. 528– 540).
  9. Pennington J, Socher R, & Manning C (2014). Glove: Global vectors for wordrepresentation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532– 1543).
  10. Turing AM (1950). Computing machinery and intelligence. Mind, 59(236), 433– 460.
  11. Wampold B, & Imel ZE (2015). The great psychotherapy debate: The evidence for what makes psychotherapy work (2nd ed.). New York: Routledge.

Cited by

Sorted by citations

Related

See all

Metadata

Authors
Goldberg, S. B., Flemotomos, N., Martinez, V. R., Tanana, M. J., Kuo, P. B., Pace, B. T., Villatte, J. L., Georgiou, P. G., Van Epps, J., Imel, Z. E., Narayanan, S. S., & Atkins, D. C.
Affiliations
  • University of Wisconsin–Madison (US)
  • University of Southern California (US)
  • University of Utah (US)
  • University of Washington (US)
ORCID
Venue
Journal of Counseling Psychology
Publisher
American Psychological Association (APA)
Published
2020-07
Volume
67
Issue
4
Pages
438-448
ISSN
  • 0022-0167 (ISSN-L)
  • 1939-2168
DOI
10.1037/cou0000382
PMC
PMC7393999
OpenAlex
W3040438665
Cited by
141
References
72
Citations by year
2026: 24 · 2025: 30 · 2024: 24 · 2023: 23 · 2022: 15 · 2021: 19 · 2020: 5
FWCI
6.19
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Counseling Psychology (publishedVersion, other-oa)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institutes of Health (R01/ AA018673)
  • University of Wisconsin-Madison
Topics
  • Social Sciences › Psychology › Clinical Psychology › Psychotherapy Techniques and Applications
  • Social Sciences › Psychology › Social Psychology › Mental Health via Writing
  • Social Sciences › General Social Sciences › Computational and Text Analysis Methods
Keywords
machine learning, natural language processing, therapeutic relationship, therapeutic process, session recording, speech recognition, psychotherapy outcome assessment, automated assessment, counseling center clients
MeSH terms
Machine Learning, Therapeutic Alliance, Adolescent, Adult, Mental Disorders, Counseling, Female, Humans, Male, Natural Language Processing, Professional-Patient Relations, Psychotherapy, Psychotherapeutic Processes, Universities, Biomedical Research, Young Adult

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review