Analysis of Demographic and Practice Characteristics of Psychiatrists in Three Canadian Provinces: Analyse des caractéristiques démographiques et de la pratique des psychiatres dans trois provinces canadiennes
Bibliographic record
Abstract
ObjectiveTo describe demographic and practice characteristics of psychiatrists in British Columbia (BC), Manitoba (MB) and Ontario (ON) and explore how practice characteristics change by psychiatrist sex/gender and years since medical school graduation.MethodWe conducted a repeated cross-sectional study of all practising psychiatrists who had patient interactions and submitted billings from the fiscal years (FY) 2012/2013 to 2021/2022 using linked administrative data in BC, MB and ON. Psychiatrist demographic variables included age, sex/gender, years since medical school graduation and their practice location. Psychiatrist practice characteristics included visit and patient volume, service settings and patient diagnoses. We used measures of central tendency to describe demographic and practice characteristics and quantify change over time using percentage change.ResultsThe number of psychiatrists increased from 2012/2013 to 2021/2022 (percentage change, BC: 15.4%, MB: 20.0%, ON: 11.8%) and kept up with population increases, shown by stable per-capita supply of psychiatrists. The median age of psychiatrists in all provinces decreased over the study period. The percentage of female psychiatrists in practice increased in all provinces, but more in BC and ON than in MB. On average, psychiatrists are seeing a greater number of patients per year in 2021/2022 than in 2012/2013 (percentage change, BC: 14.6%, MB: 6.5%, ON: 11.1%), and more than half of patients are seen, on average, for one or two visits in all three provinces. More patients receive substance use and psychosis diagnoses over the 10-year study.ConclusionsDuring the past decade, psychiatric practice characteristics show modest changes despite changing psychiatrist demographics and subtle shift towards more consultative practices. While provinces demonstrated similar trends, differences underscore the importance of conducting pan-Canadian analyses to highlight particularities in workforce patterns.Plain Language Summary TitleDescribing 10-year change in psychiatrists' demographics and practice characteristics in British Columbia, Manitoba and Ontario.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".