Helpful and Hindering Events in Internet-Delivered Cognitive Behavioural Treatment for Generalized Anxiety
Bibliographic record
Abstract
BACKGROUND: Anxiety disorders are a highly prevalent cause of impairment globally with generalized anxiety disorder (GAD) sharing many features with other anxiety disorders. AIMS: The present study investigated the helpful and hindering events and impacts for individuals with generalized anxiety who engaged with a supported 6-week online intervention based on cognitive behavioural therapy (iCBT). METHOD: Participants (n = 36) completed the Helpful and Hindering Aspects of Therapy (HAT) for each session. A descriptive-interpretative framework was used to analyse the data. RESULTS: Helpful events were identified by participants as CBT techniques including psychoeducation, monitoring, cognitive restructuring and relaxation, and found supporter interaction, mindfulness and reading personal stories helpful. The associated impacts were identified as support and validation; behavioural change/applying coping strategies; clarification, awareness, and insight; reassurance/relief; and self-efficacy/empowerment. Hindering events were identified as treatment content/form; and amount of work/technical issues, which led to impacts such as frustration/irritation; increased anxiety; and isolation. CONCLUSION: The implications of the results, potential future directions of research and limitations of the study are discussed.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".