Practical considerations for the development of a psychoeducation programme: lessons from the Cardiff experience
Bibliographic record
Abstract
Bipolar disorder is characterised by episodes \nof depression and mania or hypomania. \nMost patients tend to experience predominantly \nchronic depressive symptoms or recurrent depressive \nepisodes with the result that bipolar depression \ncan be misdiagnosed as major depressive \ndisorder. This can lead to inappropriate medication \nchoices and increased healthcare costs (1, 2). \nPsychoeducational interventions for bipolar \ndisorder – particularly group interventions – that \nare delivered alongside medication, are known to \nbe effective in reducing rates of relapse and can \nhave a positive impact by helping patients to \ndevelop a range of long-term, self-management \nskills
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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 | 0.027 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".