Canadian psychiatry utilization trends
Bibliographic record
Abstract
Introduction The number of psychiatrists continues to grow in Canada. Patient psychiatry utilization statistics, including reasons for termination of such services, are important factors that have the potential to impact future Canadian and international psychiatry service policies and practices. In addition, understanding the reasons for psychiatry service termination is necessary to improve service quality and effectiveness. Aims This study focused on utilization trends, perceived effectiveness of psychiatry services, and reasons for termination of psychiatry services in Canada. Method Prevalence of psychiatry service use, perceived effectiveness, and reasons for termination of such services were investigated in a Canadian sample (n = 25,113). Prevalence rates were investigated by geography, sex, and age. Data were self-reported and collected through a national Canadian phone survey focused on mental and physical health. Results Results highlight that a small percentage of participants reported utilizing psychiatry services. The majority of participants using such services perceived them as useful. Across geographical regions, reasons for discontinuing services were most often related to completing treatment, feeling better, or not seeing the treatment as helpful. Conclusions This study explored psychiatry utilization trends, perceived psychiatry effectiveness, and reasons for patient termination of such services. Results are explored through a geographical region breakdown, sex differences, and age stratification. Implications for policy, practice, and training are discussed from a Canadian and international perspective. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".