Supporting career development for early‐ and mid‐career professionals working in the bipolar disorder field: Key initiatives to be implemented by the International Society for Bipolar Disorders Early‐ and Mid‐Career Committee
Bibliographic record
Abstract
KMD uses software provided free of charge by Scientific Brain Training Pro for Cognitive Remediation trials. KMD would like to acknowledge salary support from the Health Research Council of New Zealand (Sir Charles Hercus Health Research Fellowship; ref: 19/082). OMD 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 Nutracuticals, NutritionCare, and Bioceuticals. OMD is an Investigator on the Medical Research Futures Fund 2020 Million Minds Mission—Mental Health Australia General Clinical Trial Network (MAGNET). NV has received financial support for CME activities and travel funds from the following entities (unrelated to the present work): Angelini, Janssen-Cilag, Lundbeck, and Otsuka. JJP acknowledges financial support from the CONAHCYT's National System of Researchers (SNI) of Mexico. RHBM acknowledges salary support from Academic Scholar Awards from the Department of Psychiatry at the University of Toronto and Sunnybrook Health Sciences Centre and an honorarium from Medscape. TVR would like to acknowledge the financial support of an Al and Val Rosenstrauss Fellowship from the Rebecca L Cooper Medical Research Foundation. SHS would like to acknowledge the financial support of the National Institute of Mental Health (L30MH127613 and K23MH13601), the Heinz C. Prechter Bipolar Research Fund, and the Eisenberg Family Depression Center at the University of Michigan. The data that support the findings of this paper are available from the corresponding author upon reasonable request.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".