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Record W2070353580 · doi:10.4103/0019-5545.69213

Psychiatrists and neuroscientists of Indian origin in Canada: Glimpses

2010· article· en· W2070353580 on OpenAlexaffabout
Amresh Shrivastava, D Natarajan

Bibliographic record

VenueIndian Journal of Psychiatry · 2010
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsWestern UniversityLawson Health Research Institute
Fundersnot available
KeywordsMental healthExcellencePsychiatrySpecialtyFace (sociological concept)PsychologyWork (physics)MedicineMedical educationSociologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score0.921

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.249
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2010
Admission routes2
Has abstractyes

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