Scoping review on natural language processing applications in counselling and psychotherapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it