Transdiagnostic internet-delivered cognitive behaviour therapy: Feasibility of a motivational interviewing resource
Bibliographic record
Abstract
Background: Despite growing evidence for the effectiveness of internet-delivered cognitive behaviour therapy (ICBT), engagement and treatment outcomes are lower for some clients. Online motivational interviewing (MI) has been investigated prior to offering ICBT to facilitate engagement and outcomes, but only appears to improve engagement. Purpose: This feasibility study investigated the potential use of a brief MI resource offered during ICBT rather than before, by examining: (1) use of the resource; (2) client and treatment variables associated with use; (3) whether use of the resource was associated with improved engagement and outcomes; and (4) how those who used the resource evaluated it. Method: This study used data collected from 763 clients enrolled in an ICBT course. Symptoms related to depression, anxiety and disability were assessed at pre- and post-treatment. The website tracked treatment engagement. Clients completed an MI resource evaluation measure at post-treatment. Results: Approximately 15% of clients used the resource. Clients who were older, had higher education, scored in the clinical range on depression, and scored lower on anxiety at pre-treatment were more likely to use the resource. Those who reported using the resource had higher engagement (i.e., more lessons and messages) in ICBT, but lower improvement in disability post-treatment. Positive feedback on the MI resource outweighed negative feedback, with 94 % of clients identifying a positive aspect of the resource and 66 % of clients reporting making changes in response to the resource. Overall, the MI resource appears to be used by and perceived as beneficial by a small portion of clients who complete ICBT. The study provides insight into use of the resource and directions for future research related to MI and ICBT.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".