Implementation and Utilisation of Telepsychiatry in Ontario: A Population-Based Study
Bibliographic record
Abstract
OBJECTIVE: Rural areas in Ontario have fewer psychiatrists, making access to specialist mental health care challenging. Our objective was to characterise psychiatrists delivering and patients receiving telepsychiatry in Ontario and to determine the number of patients who accessed a psychiatrist via telepsychiatry following discharge from psychiatric hospitalisation. METHOD: We conducted a serial panel study to evaluate the characteristics of psychiatrists providing telepsychiatry from April 2007 to March 2013. In addition, we conducted a cross-sectional study for fiscal year 2012-2013 to examine telepsychiatry patient characteristics and create an in-need patient cohort of individuals with a recent psychiatric hospitalisation that assessed if they had follow-up with a psychiatrist in person or through telepsychiatry within 1 year of discharge. RESULTS: In fiscal year 2012-2013, a total of 3801 people had 5635 telepsychiatry visits, and 7% ( n = 138) of Ontario psychiatrists provided telepsychiatry. Of the 48,381 people identified as in need of psychiatric care, 60% saw a local psychiatrist, 39% saw no psychiatrist, and less than 1% saw a psychiatrist through telepsychiatry only or telepsychiatry in addition to local psychiatry within a year. Three northern regions had more than 50% of in-need patients fail to access psychiatry within 1 year. CONCLUSIONS: Currently, relatively few patients and psychiatrists use telepsychiatry. In addition, patients scarcely access telepsychiatry for posthospitalisation follow-up. This study, which serves as a preliminary baseline for telepsychiatry in Ontario, demonstrates that telepsychiatry has not evolved systematically to address need and highlights the importance of system-level planning when implementing telepsychiatry to optimise 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".