Association between physician characteristics and practice-level uptake of paediatric virtual mental healthcare: a population-based study
Bibliographic record
Abstract
OBJECTIVE: To examine physician factors associated with practice-level uptake of virtual mental healthcare for children and adolescents. DESIGN, SETTING AND PARTICIPANTS: A population-based data linkage study of a cohort of all physicians (n=12 054) providing outpatient mental healthcare to children and adolescents (aged 3-17 years, n=303 185) in a single-payer provincial health system in Ontario, Canada from 1 July 2020 to 31 July 2021. EXPOSURES: Physician characteristics including gender, age, specialty, location of training, practice region, practice size and overall and mental health practice size. MAIN OUTCOMES: Practice-level proportion of outpatient virtual care provided: (1) mostly in-person (<25% virtual care), (2) hybrid (25%-99% virtual care) or (3) exclusively virtual (100% virtual care). Multinomial logistic regression models tested the association between practice-level virtual care provided and physician characteristics. RESULTS: Among physicians, 1589 (13.2%) provided mostly in-person mental healthcare with 8714 (67.8%) providing hybrid care, and 2291 (19.0%) providing exclusively virtual care. The provision of exclusive virtual care (vs mostly in-person) was associated with female sex (adjusted OR (aOR) 1.97, 95% CI 1.70 to 2.27 (ref: male)), foreign training (aOR 1.27, 95% CI 1.07 to 1.50 (ref: Canadian-trained)), family physicians (aOR 2.05, 95% CI 1.56 to 2.69 (ref: psychiatrist)) and reversely associated with large practice size (aOR 0.32, 95% CI 0.25 to 0.40 (ref smallest quintile)). Mostly in-person care was associated with older age physicians (71+ years) and practice outside the Toronto region. CONCLUSIONS AND RELEVANCE: In a single-payer universal healthcare system that remunerates physicians using the same fee structure for in-person and virtual outpatient care, there is heterogeneity in utilisation of virtual care that is associated with provider factors. This practice variation, with limited evidence on effectiveness and appropriate contexts for virtual care use, suggests there may be opportunity for further outcomes research and guidance on appropriate context for paediatric virtual mental healthcare delivery.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".