Empathy as a crucial skill in disrupting disparities in global brain health
Bibliographic record
Abstract
Brain health refers to the state of a person's brain function across various domains, including cognitive, behavioral and motor functions. Healthy brains are associated with better individual health, increased creativity, and enhanced productivity. A person's brain health is intricately connected to personal, social and environmental factors. Racial, ethnic, and social disparities affect brain health and on the global scale these disparities within and between regions present a hurdle to brain health. To overcome global disparities, greater collaboration between practitioners and healthcare providers and the people they serve is essential. This requires cultural humility driven by empathy. Empathy is a core prosocial value, a cognitive-emotional skill that helps us understand ourselves and others. This position paper aims to provide an overview of the vital roles of empathy, cooperation, and interdisciplinary partnerships. By consciously integrating this understanding in practice, leaders can better position themselves to address the diverse challenges faced by communities, promote inclusivity in policies and practices, and further more equitable solutions to the problem of global brain health.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".