Impact of Activation Therapy for Inpatients With Major Depression: Primary and Secondary Outcomes From a Randomised Controlled Trial
Bibliographic record
Abstract
INTRODUCTION: Inpatient depression is associated with high morbidity and significant cognitive impairment. Inpatient treatment often focuses on short-term stabilization with medication. Readmission rates are high. We examined the impact of a novel psychological intervention, activation therapy (AT, Behavioural Activation combined with Cognitive Activation), versus treatment as usual (TAU) on readmission rates, and cognitive, functional, and depression outcomes, in inpatient depression. METHOD: A randomised controlled trial in adults hospitalised with a major depressive episode. Inpatients were randomised to AT (8 individual sessions over 2 weeks) or not (TAU). Key time points were baseline (on admission) and 14 weeks after baseline. The primary outcome was psychiatric hospital readmission rates within 12 weeks of discharge. Secondary outcomes were cognition, general functioning, depression, and 'deactivation' symptoms (change from baseline to 14 weeks). RESULTS: Ninety-seven individuals were randomised to AT (n = 47) or TAU (n = 50). Readmission rates did not differ between treatment arms (34% vs. 40%; OR = 0.76, CI = 0.30-1.90). Significant improvements for verbal learning and memory (d = 0.42) and general functioning (d = 0.58) were in favour of the AT versus TAU arms. Per protocol analysis showed additional significant effects of AT on psychomotor speed (d = 0.64) and clinician-rated depression symptoms (d = 0.56). No significant effects were observed for other secondary outcomes (subjective cognition, self-reported depression symptoms, and deactivation symptoms). CONCLUSIONS: The AT intervention showed durable, pro-cognitive effects. Further adaptations of AT, such as the addition of maintenance sessions as patients transition to community-based care, need exploring.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".