The predictive capacity of self-reported motivation vs. early observed motivational language in cognitive behavioural therapy for generalized anxiety disorder
Bibliographic record
Abstract
Client motivation to change is often considered a key factor in psychotherapy. To date, research on this client construct has largely relied on self-report, which is prone to response bias and ceiling effects. Moreover, self-reported motivation has been inconsistently related to treatment outcome. Early observed client in-session language may be a more valid measure of initial motivation and thus a promising predictor of outcome. The predictive ability of motivational factors has been examined in addiction treatment but has been limited in other populations. Addressing this lack, the present study investigated 85 clients undergoing cognitive behavioural therapy (CBT) alone and CBT infused with motivational interviewing (MI-CBT) for severe generalized anxiety disorder. There were two aims: (1) to compare the predictive capacity of motivational language vs. two self-report measures of motivation on worry reduction and (2) to examine the influence of treatment condition on motivational language. Findings indicated that motivational language explained up to 35% of outcome variance, event 1-year post-treatment. Self-reported motivation did not predict treatment outcome. Moreover, MI-CBT was associated with a significant decrease in the most detrimental type of motivational language compared to CBT alone. These findings support the importance of attending to in-session motivational language in CBT and learning to respond to these markers using motivational interviewing.
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.
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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".