From knowledge gaps to career opportunities: The early‐ to Mid‐Career Committee's impact on increasing resources for bipolar disorder professionals
Bibliographic record
Abstract
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 honourarium from Medscape. 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 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). KMD uses free-of-charge software 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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.081 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.021 | 0.005 |
| Scholarly communication | 0.022 | 0.014 |
| Open science | 0.006 | 0.045 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.059 | 0.011 |
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".