Bioethical and critical consciousness in clinical translational neuroscience
Bibliographic record
Abstract
Clinical translational neuroscience (CTN) is positioned to generate novel discoveries for advancing treatments for mental health disorders, but it is held back today by the siloing of bioethical considerations from critical consciousness. In this article, we suggest that bioethical and critical consciousness can be paired to intersect with structures of power within which science and clinical practice are conducted. We examine barriers to the adoption of neuroscience findings in mental health from this perspective, especially in the context of current collective attention to widespread disparities in the access to and outcomes of mental health services, lack of representation of marginalized populations in the relevant sectors of the workforce, and the importance of knowledge that draws upon multicultural perspectives. We provide 10 actionable solutions to confront these barriers in CTN research, as informed by existing frameworks such as structural competency, adaptive calibration models, and community-based participatory research. By integrating critical consciousness with bioethical considerations, we believe that practitioners will be better positioned to benefit from cutting-edge research in the biological and social sciences than in the past, alert to biases and equipped to mitigate them, and poised to shepherd in a robust generation of future translational therapies and practitioners.
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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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.025 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 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".