The Chronic Conditions Course: A Randomised Controlled Trial of an Internet-Delivered Transdiagnostic Psychological Intervention for People with Chronic Health Conditions
Bibliographic record
Abstract
INTRODUCTION: Psychological adjustment to chronic health conditions is important, as poor adjustment predicts a range of adverse medical and psychosocial outcomes. Psychological treatments demonstrate efficacy for people with chronic health conditions, but existing research takes a disorder-specific approach and they are predominately delivered in face-to-face contexts. The internet and remotely delivered treatments have the potential to overcome barriers to accessing traditional face-to-face treatment. OBJECTIVE: The current study examined the efficacy and acceptability of an internet-delivered transdiagnostic psychological intervention to promote adjustment to illness, based on cognitive behaviour therapy principles. METHODS: In a two-arm randomised controlled trial, participants (n = 676) were randomly allocated to the 8-week intervention or a waitlist control. Treatment included five core lessons, homework tasks, additional resources, and weekly contact with a psychologist. Primary outcomes included depression, anxiety, and disability, assessed at pre-treatment, post-treatment, 3-month follow-up, and 12-month follow-up. RESULTS: The treatment group reported significantly greater improvements in depression (between-groups d = 0.47), anxiety (d = 0.32), and disability (d = 0.17) at post-treatment (all ps <0.001). Improvements were sustained over the 3-month and 12-month follow-ups. High treatment completion rates (69%) and levels of satisfaction (86%) were reported by participants in treatment. The intervention required a mean clinician time of 56.70 min per participant. CONCLUSIONS: The findings provide preliminary and tentative support for the potential of internet-delivered transdiagnostic interventions to promote adjustment to chronic health conditions. Further research using robust control groups, and exploring the generalisability of findings, is needed before firm conclusions can be drawn.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".