Sociodemographic Differences in Physician-Based Mental Health and Virtual Care Utilization and Uptake of Virtual Care Among Children and Adolescents During the COVID-19 Pandemic in Ontario, Canada: A Population-Based Study
Bibliographic record
Abstract
OBJECTIVE: We sought to evaluate the relationship between social determinants of health and physician-based mental healthcare utilization and virtual care use among children and adolescents in Ontario, Canada, during the COVID-19 pandemic. METHODS: = 2.5 million) used linked health and demographic administrative data in Ontario, Canada (2017-2021). Multivariable Poisson regressions with generalized estimating equations compared rates of outpatient physician-based mental healthcare use during the first year of the COVID-19 pandemic with expected rates based on pre-COVID patterns. Analyses were conducted by socioeconomic status (material deprivation quintiles of the Ontario Marginalization index), urban/rural region of residence, and immigration status. RESULTS: Overall, pediatric physician-based mental healthcare visits were 5% lower than expected (rate ratio [RR] = 0.95, 95% confidence interval [CI], 0.92 to 0.98) among those living in the most deprived areas in the first year of the pandemic, compared with the least deprived with 4% higher than expected rates (RR = 1.04, 95% CI, 1.02 to 1.06). There were no differences in overall observed and expected visit rates by region of residence. Immigrants had 14% to 26% higher visit rates compared with expected from July 2020 to February 2021, whereas refugees had similarly observed and expected rates. Virtual care use was approximately 65% among refugees, compared with 70% for all strata. CONCLUSION: During the first year of the pandemic, pediatric physician-based mental healthcare utilization was higher among immigrants and lower than expected among those with lower socioeconomic status. Refugees had the lowest use of virtual care. Further work is needed to understand whether these differences reflect issues in access to care or the need to help inform ongoing pandemic recovery planning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".