Psychiatrists and neuroscientists of Indian origin in Canada: Glimpses
Bibliographic record
Abstract
Psychiatrists of Indian origin are popular in Canada, being firmly rooted in the Canadian mental health system, and they have been making considerable contributions internationally. The Indian Psychiatric Society has long been collaborating with and inviting contributions from overseas Indian psychiatrists, particularly those in academics, and this collaboration has fructified well. There are several different challenges these psychiatrists have had to face in their own specialty work, with having to adjust to a new culture, new ways of living, and new ways of work. Our colleagues of Indian origin have demonstrated excellence in almost all fields of mental health and neurosciences. There are many popular teachers, outstanding researchers, and psychiatrists in community practice and community development. The Early Psychosis Program, Mood and Anxiety Program, Perinatal Psychiatry, Women's Mental Health, and Postpartum Mental Health are some of their key areas of research. Our basic scientists are involved in experimental design, neurochemistry, imaging, and genetics, where they have made their mark with acclaim. This article highlights some of the achievements of a few members and is by no means completely representative of the entire work that psychiatrists of Indian origin are doing in Canada, providing readers with a glimpse of our labors away from home.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".