Examining Change in the Frequency of Adaptive Actions as a Mediator of Treatment Outcomes in Internet-Delivered Therapy for Depression and Anxiety
Bibliographic record
Abstract
Adaptive actions, including healthy thinking and meaningful activities, have been associated with emotional wellbeing. The Things You Do Questionnaire—21 item (TYDQ-21) has recently been created to measure the frequency of such actions. A study using the TYDQ-21 found that adaptive actions increased across Internet-delivered therapy for symptoms of depression and anxiety, and higher TYDQ-21 scores were associated with lower psychological distress at post-treatment. The current study examined the relationships between adaptive actions and psychological distress among adults (n = 1114) receiving Internet-delivered therapy as part of routine care in Canada, and explored whether adaptive actions mediated reductions in depression and anxiety. As hypothesised, adaptive actions increased alongside reductions in depression and anxiety symptoms from baseline to post-treatment. Treatment effects were consistent when the intervention was provided with regular weekly therapist support or with optional weekly therapist support, and some (but not all) types of adaptive actions had a mediating effect on change in depressive symptoms. The present findings support further work examining adaptive actions as a mechanism of change in psychotherapy, as well as the utility and scalability of Internet-delivered treatments to target and increase adaptive actions with the aim of improving mental health.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".