Recommendations for a better understanding of sex and gender in neuroscience of mental health
Bibliographic record
Abstract
There are prominent sex/gender differences in the prevalence, expression and lifespan course of mental health and neurodiverse conditions. Yet the underlying sex and gender related mechanisms and their interactions are still not fully understood. This lack of knowledge has harmful consequences for those suffering from mental health problems. Hence, we set up a co-creation session in a one-week workshop with a multidisciplinary team of 25 researchers, clinicians and policy makers, to identify the main barriers in sex and gender research in neuroscience of mental health. Based on this work, we here provide recommendations for methodologies, translational research and stakeholder involvement. These include guidelines for recording, reporting, analysis beyond binary groups, and open science. Improved understanding of sex and gender related mechanisms in neuroscience may benefit public health as this is an important step towards precision medicine and may function as an archetype for studying diversity.
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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 | 0.148 | 0.404 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.015 | 0.035 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.020 | 0.027 |
| Insufficient payload (model declined to judge) | 0.084 | 0.030 |
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".