Evaluation of an online cognitive behavioural therapy program by patients with traumatic brain injury and depression
Bibliographic record
Abstract
PRIMARY OBJECTIVE: The most frequently reported psychiatric symptom after traumatic brain injury (TBI) is depression. This study examined whether internet-delivered cognitive behaviour therapy (CBT) could be appropriate and effective for patients with mild or moderate TBI and depression. METHODS AND PROCEDURES: Patients were recruited for an at-home, 6-week internet-based CBT program (MoodGYM). Participants were assessed during this period by weekly telephone calls and at 12 months post-enrolment. Intervention completion rates, predictors of adherence, user feedback and changes in scores on validated depression scales were assessed. MAIN OUTCOMES AND RESULTS: Twenty-one patients were recruited: 64% and 43% completed the 6-week intervention and the 12-month follow-up, respectively. Adherence rates were not predicted by demographic or injury characteristics in this small sample. Patients identified reading, memory and comprehension requirements as limitations of the program. Scores on the depression scales were significantly decreased upon completion of the intervention and at the 12-month follow-up. CONCLUSIONS: The MoodGYM program may be effective for treating symptoms of depression in patients with TBI. While adherence rates were not predicted by age, education level or injury severity, demands upon memory and concentration which may already be compromised in these patients need to be considered.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".