Targeting the Training and Educational Priorities of Bipolar Disorder‐Focused Early and Mid‐Career Researchers and Clinicians
Bibliographic record
Abstract
N.V. has received financial support for CME activities and travel funds from the following entities (unrelated to the present work): Angelini, Janssen-Cilag, Lundbeck, Otsuka. R.M. reported receiving grants from the American Foundation for Suicide Prevention, the TD Pooler Fund, and the Sunnybrook Foundation, an Academic Scholar Award from the Department of Psychiatry at Sunnybrook Health Sciences Centre and the University of Toronto, and an honorarium from Medscape outside of the submitted work. O.D. is Associate Professor (Research) Deakin University and has received grant support from the Brain and Behavior Foundation, Simons Autism Foundation, Stanley Medical Research Institute, Deakin University, Lilly, NHMRC, and ASBDD/Servier. She has also received in-kind support from BioMedica Nutraceuticals, NutritionCare, and Bioceuticals. None of these relationships have any relevance to the current publication. F.G. received research funding from the BBRF Foundation (NARSAD Young Investigator Award/P&S Fund Investigator), the Canadian Menopausal Society/Pfizer Research Award, Queen's University Faculty of Health Science, SEAMO and Queen's University Department of Psychiatry internal grants. He received honoraria as a speaker/consultant in the past from Abbvie, Lundbeck, and Otsuka. All these funds were unrelated to the current manuscript. K.M.D. uses software provided free of charge by Scientific Brain Training Pro for Cognitive Remediation trials. K.M.D. would like to acknowledge salary support from the Health Research Council of New Zealand (Sir Charles Hercus Health Research Fellowship; ref.: 19/082). Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
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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 | 0.031 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.025 | 0.010 |
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".