Harnessing the potential of community-based participatory research approaches in bipolar disorder
Bibliographic record
Abstract
BACKGROUND: Despite the rapid growth in the sophistication of research on bipolar disorder (BD), the field faces challenges in improving quality of life (QoL) and symptom outcomes, adapting treatments for marginalized communities, and disseminating research insights into real-world practice. Community-based participatory research (CBPR)-research that is conducted as a partnership between researchers and community members-has helped address similar gaps in other health conditions. This paper aims to improve awareness of the potential benefits of CBPR in BD research. METHODS: This paper is a product of the International Society for Bipolar Disorders (ISBD) Taskforce on Community Engagement which includes academic researchers, healthcare providers, people with lived experience of BD, and stakeholders from BD community agencies. Illustrative examples of CBPR in action are provided from two established centres that specialize in community engagement in BD research: the Collaborative RESearch Team to study psychosocial issues in BD (CREST.BD) in Canada, and the Spectrum Centre for Mental Health Research in the United Kingdom. RESULTS AND DISCUSSION: We describe the philosophy of CBPR and then introduce four core research areas the BD community has prioritized for research: new treatment approaches, more comprehensive outcome assessments, tackling stigma, and enhanced understanding of positive outcomes. We then describe ways in which CBPR is ideal for advancing each of these research areas and provide specific examples of ways that CBPR has already been successfully applied in these areas. We end by noting potential challenges and mitigation strategies in the application of CBPR in BD research. CONCLUSIONS: We believe that CBPR approaches have significant potential value for the BD research community. The observations and concerns of people with BD, their family members, and supports clearly represent a rich source of information. CBPR approaches provide a collaborative, equitable, empowering orientation to research that builds on the diversity of strengths amongst community stakeholders. Despite the potential merits of this approach, CBPR is as yet not widely used in the BD research field, representing a missed opportunity.
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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 | 0.235 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.015 | 0.025 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.005 | 0.031 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".