Optimizing delivery of recovery‐oriented online self‐management strategies for bipolar disorder: a review
Bibliographic record
Abstract
OBJECTIVES: Self-management is emerging as a viable alternative to difficult-to-access psychosocial treatments for bipolar disorder (BD), and has particular relevance to recovery-related goals around empowerment and personal meaning. This review examines data and theory on BD self-management from a recovery-oriented perspective, with a particular focus on optimizing low-intensity delivery of self-management tools via the web. METHODS: A critical evaluation of various literatures was undertaken. Literatures on recovery, online platforms, and self-management in mental health and BD are reviewed. RESULTS: The literature suggests that the self-management approach aligns with the recovery framework. However, studies have identified a number of potential barriers to the utilization of self-management programs for BD and it has been suggested that utilizing an online environment may be an effective way to surmount many of these barriers. CONCLUSIONS: Online self-management programs for BD are rapidly developing, and in parallel the recovery perspective is becoming the dominant paradigm for mental health services worldwide, so research is urgently required to assess the efficacy and safety of optimization methods such as professional and/or peer support, tailoring and the development of 'online communities'.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".