Activation therapy for the treatment of inpatients with depression – protocol for a randomised control trial compared to treatment as usual
Bibliographic record
Abstract
BACKGROUND: Inpatients with depression have a poor long term outcome with high rates of suicide, high levels of morbidity and frequent re-admission. Current treatment often relies on pharmacological intervention and focuses on observation to maintain safety. There is significant neurocognitive deficit which is linked to poor functional outcomes. As a consequence, there is a need for novel psychotherapeutic interventions that seek to address these concerns. METHODS: We combined cognitive activation and behavioural activation to create activation therapy (AT) for the treatment of inpatient depression and conducted a small open label study which demonstrated acceptability and feasibility. We propose a randomised controlled trial which will compare treatment as usual (TAU) with TAU plus activation therapy for adult inpatients with a major depressive episode. The behavioural activation component involves therapist guided re-engagement with previously or potentially rewarding activities. The cognitive activation aspect utilises computer based exercises which have been shown to improve cognitive function. DISCUSSION: The proposed randomised controlled trial will examine whether or not the addition of this therapy to TAU will result in a reduced re-hospitalisation rate at 12 weeks post discharge. Subjective change in activation and objectively measured change in activity levels will be rated, and the extent of change to neurocognition will be assessed. TRIAL REGISTRATION: Unique trial number: U1111-1190-9517. Australian New Zealand Clinical Trials Registry (ANZCTR) number: ACTRN12617000024347p .
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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 | 0.031 | 0.032 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.049 | 0.007 |
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".