Ten Priorities for Research Addressing the Intersections of Brain Injury, Mental Health and Addictions: A Stakeholder‐Driven Priority‐Setting Study
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to engage key stakeholders in a health research priority-setting process to identify, prioritize and produce a community-driven list of research questions addressing intersectional issues on mental health and addictions (MHA) in acquired brain injury (ABI). METHODS: A multiphasic health research priority-setting process was co-designed and executed with community-based stakeholders, including researchers, health professionals, clinicians, service providers, representatives from brain injury associations, policy makers and people with lived experience of ABI and MHA, including patients and their family members. Stakeholders' ideas led to the generation of research questions, which were prioritized at a 1-day workshop. RESULTS: Fifty-nine stakeholders participated in the priority-setting activity during the workshop, which resulted in a rank-ordered list of the top 10 questions for research addressing the intersections of ABI and MHA. Questions identified touched on several pressing issues (e.g., opioid crisis, homelessness), encompassed multiple subtypes of ABI (e.g., hypoxic-ischaemic, mild traumatic), and involved different domains (e.g., identification, intervention) of health research. CONCLUSIONS: This community-driven health research priority-setting study identified and prioritized research questions addressing the intersections of ABI and MHA. Researchers and funding agencies should use this list to inform their agendas and address stakeholders' most urgent needs, fostering meaningful improvements to clinical services. PATIENT OR PUBLIC CONTRIBUTION: An 11-person working group comprised of people with lived experience, service providers, researchers, healthcare professionals and other key stakeholders collaboratively developed and informed the scope, design, methodology and interpretation of this study. Over 50 community-based stakeholders contributed to the research priority-setting activity. One co-author is a person with lived experience.
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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 | 0.354 | 0.182 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.024 | 0.012 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.007 | 0.033 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".