796Is Mindfulness a more effective intervention than Psychoeducation for reducing Bipolar Disorder-related symptoms of depression?
Bibliographic record
Abstract
Abstract Background The primary objective is to test the effectiveness of a novel online quality of life (QoL) intervention tailored for people with late stage (≥ 10 episodes) bipolar disorder (BD) compared with psychoeducation. Relative to early stage individuals, this late stage group may not benefit as much from existing psychosocial treatments. Methods An NH&MRC funded international RCT to compare the effectiveness of two 5-week adjunctive online self-management interventions: Mindfulness for Bipolar 2.0 and an active control (Psychoeducation for Bipolar). A total of 300 participants were recruited primarily via social media channels. Evaluations occurred at pre- and post- treatment, and at 3- and 6- months follow-up. A secondary outcome measure was BD-related symptoms (depression). A longitudinal analysis was conducted using random effects mixed models. Results Preliminary results suggest no change in mean QIDS_total over time (p = 0.891). Nor does there appear to be a difference in groups (B coeff= 0.47, 95%CI (-0.60, 1.55), p = 0.613) and no difference in intervention groups over time (p = 0.828). Conclusions The effectiveness of a novel QoL focused, mindfulness based, online guided self-help intervention for late stage BD does not appear to have been any more effective than a psychoeducation intervention for reducing depression. Key messages There may be no significant benefit from using a mindfulness based, online guided self-help intervention over a psychoeducation intervention to reduce the BD symptoms of depression in late stage Bipolar disorder.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".