Linguistic Analysis of Communication in Therapist-Assisted Internet-Delivered Cognitive Behavior Therapy for Generalized Anxiety Disorder
Bibliographic record
Abstract
Therapist-assisted Internet-delivered cognitive behavior therapy (ICBT) involves elements of expressive writing through secure messaging with a therapist. Expressive writing has been associated with psychological and physical health benefits in past research; furthermore, certain linguistic dimensions in expressive writing have been identified as particularly beneficial to health, such as less frequent use of negative emotion words and greater use of positive emotion words. No research, to date, has analyzed linguistic dimensions in client communication over the course of therapist-assisted ICBT for individuals with symptoms of generalized anxiety. This naturalistic study examined messages sent to therapists during the course of ICBT using linguistic analysis, and explored covariation of word use with symptom improvement. Data were obtained from patients with symptoms of generalized anxiety (N = 59) who completed 12 modules of therapist-assisted ICBT and rated symptoms of anxiety, depression, and panic at the beginning of each module. Linguistic analysis categorized text submitted to therapists into different word categories. Results found that patients' use of negative emotion, anxiety, causation, and insight words reduced over the course of treatment, while past tense words increased. Furthermore, negative emotion words significantly covaried with symptom ratings over the course of treatment. While causal statements cannot be made, findings improve our understanding of patient communication in ICBT and suggest that the further study of linguistic dimensions as psychological indicators and the potential utility of expressive writing strategies in therapist-assisted ICBT may be worthwhile.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".