Regional Variation in Supply and Use of Psychiatric Services in 3 Canadian Provinces: Variation régionale de l’offre de services psychiatriques et de leur utilisation dans trois provinces canadiennes
Bibliographic record
Abstract
Objective To examine the patterns in the supply and use of psychiatric services in 3 Canadian provinces: British Columbia, Manitoba, and Ontario. Methods We conducted a repeated cross-sectional analysis spanning fiscal years 2012/13 to 2021/22, using patient- and psychiatrist-level data aggregated into administrative health regions. Descriptive statistics and linear regression were used to assess patterns and relationships between the per capita number of psychiatrists (“supply”) and measures of use of psychiatric services (“utilization”), including any psychiatrist contact, psychiatric consultation (1–2 visits with the same psychiatrist), and ongoing psychiatric care (3 or more visits with the same psychiatrist). Results The number of psychiatrists per capita remained stable within the 3 provinces during the study period. In 2021/22, Vancouver had the highest number in British Columbia (45 psychiatrists per 100,000 individuals), compared to 14 per 100,000 in lower-supply regions. Toronto had the highest number in Ontario (38 per 100,000), compared to 9 in lower supply regions. Winnipeg had the highest number in Manitoba (25 per 100,000), compared to 7 in the lower supply regions. In 2021/22, the per capita number of psychiatrists was moderately correlated with any psychiatrist contact ( R 2 = 0.290) and ongoing psychiatric care ( R 2 = 0.411), but weakly correlated with psychiatric consultation ( R 2 = 0.005). The relationship between supply and utilization diminishes with higher levels of regional supply. Conclusions Psychiatrists were unevenly distributed within and across provinces. While more psychiatrists are needed, the moderate and diminishing relationships between their numbers and utilization suggest that increasing this number alone is unlikely to fully address unmet needs for mental healthcare. Strategies to improve access will need to directly target uneven distributions. Further research is needed to understand the factors influencing psychiatrists’ practice choices and ways to better support them in increasing their access to care.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".