Understanding the current state of play of early intervention for bipolar disorder: Qualitative analysis of consultations with international stakeholders
Bibliographic record
Abstract
Background: Despite the burden associated with bipolar disorder (BD), research into early diagnosis and treatment of BD lags approximately 20 years behind the field of early intervention for psychosis. This study evolved through a partnership between Orygen (Melbourne, Australia) and the Daymark Foundation (Toronto, Canada). The primary focus was to answer the question: “How might we advance an early intervention approach for people at-risk of or with BD?”. Methods: Semi-structured interviews were conducted with international experts and other stakeholders in early intervention and BD, to identify challenges and barriers in early intervention approaches to BD. Results: Twenty-eight experts participated. Nine themes emerged as challenges: (i) limited recognition and understanding of BD across stakeholders; (ii) lack of definitions; (iii) poor resourcing and lack of prioritisation in funding models; (iv), absence of validated tools for diagnosis, monitoring treatment response and/or disorder progression; (v) absence of ‘big data’; (vi) scarcity of evidence-based treatments and clinical guidelines for the early stages of the disorder; (vii) obscurity around optimal service models; (viii) the need for better support and involvement of families and significant others; and (ix) the need for collaboration (across disciplines, stakeholders, and settings) to progress the field.Limitations: Of those approached, 54.9% participated in the study. Given the qualitative nature of the study, recruiting more experts to the study would not necessarily change the outcomes as data saturation was achieved. Conclusions: This work lays the foundations for developing a collaborative research framework to progress early intervention for young people with BD.
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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 | 0.035 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".