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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".