Characterizing Family Physicians Who Refer to Telepsychiatry in Ontario
Bibliographic record
Abstract
INTRODUCTION: Telepsychiatry can improve access to psychiatric services for those who otherwise cannot easily access care. Family physicians are gatekeepers to specialized care in Ontario, so it is essential to understand predictors relating to referrals to telepsychiatry to better plan services and increase telepsychiatry adoption. METHODS: This study used an annual retrospective cross-sectional study design to compare physicians who referred their patients to telepsychiatry each year from fiscal year (FY) 2008 to FY 2016. A 1-year (FY 2016) comparison of family physicians who referred to telepsychiatry (FPTs) compared to family physicians who did not refer to telepsychiatry (FPNTs) matched (1:2) by region was also conducted. Finally, we used statistical modeling to understand the predictors of referring to telepsychiatry among physicians. RESULTS: Between FY 2008 and FY 2016, the number of patients receiving telepsychiatry increased from 925 visits to 13,825 visits. Thirty-two percent of Ontario primary care physicians referred to telepsychiatry in 2016. Several characteristics were notably different between FPTs and FPNTs: FPTs were more likely to be from a residence with less than 10,000 people, to have more nurse practitioners in the practice, and to be from a family health team than FPNTs. Rostered patients of FPTs were more likely to reside in rural areas, have more clinical complexity, and to utilize more mental health services compared to FPNTs. CONCLUSIONS: There has been an increase in the use of telepsychiatry by patients and family physicians over the study period, although there remains opportunity for significant growth. Family physicians who live in rural areas, are part of an FHT, have more NPs, with more rural and complex patients were more likely to refer to telepsychiatry. As recent pro-telemedicine policies support the growth of telepsychiatry, this study will serve as an important baseline.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".