Integrating sex and gender into neurodegeneration research: A six‐component strategy
Bibliographic record
Abstract
INTRODUCTION: Despite important sex differences, there remains a paucity of studies examining sex and gender differences in neurodegeneration. The Canadian Consortium on Neurodegeneration in Aging (CCNA), a national network of researchers, provides an ideal platform to incorporate sex and gender. METHODS: CCNA's Women, Gender, Sex and Dementia program developed and implemented a six-component strategy involving executive oversight, training, research collaboration, progress report assessment, results dissemination, and ongoing manuscript review. The inclusion of sex and gender in current and planned CCNA projects was examined in two progress reporting periods in 2016. RESULTS: Sex and gender research productivity increased substantially for both preclinical (36%-45%) and human (56%-60%) cohorts. The main barrier was lack of funding. DISCUSSION: The Women, Gender, Sex and Dementia strategy resulted in a major increase of sex and gender into research on neurodegenerative disorders. This best practice model could be utilized by a wide variety of large multidisciplinary groups.
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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 | 0.401 | 0.282 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.007 | 0.043 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".