The impact of the COVID-19 pandemic on the rate of primary care visits for substance use among patients in Ontario, Canada
Bibliographic record
Abstract
The COVID-19 pandemic has led to an increase in the prevalence of substance use presentations. This study aims to assess the impact of the COVID-19 pandemic on the rate of primary care visits for substance use including tobacco, alcohol, and other drug use among primary care patients in Ontario, Canada. Diagnostic and service fee code data were collected from a longitudinal cohort of family medicine patients during pre-pandemic (March 14, 2019-March 13, 2020) and pandemic periods (March 14, 2020-March 13, 2021). Generalized linear models were used to compare the rate of substance-use related visits pre-pandemic and during the pandemic. The effects of demographic characteristics including age, sex, and income quintile were also assessed. Relative to the pre-pandemic period, patients were less likely to have a primary care visit during the pandemic for tobacco-use related reasons (OR = 0.288, 95% CI [0.270-0.308]), and for alcohol-use related reasons (OR = 0.851, 95% CI [0.780-0.929]). In contrast, patients were more likely to have a primary care visit for other drug-use related reasons (OR = 1.150, 95% CI [1.080-1.225]). In the face of a known increase in substance use during the COVID-19 pandemic, a decrease in substance use-related primary care visits likely represents an unmet need for this patient population. This study highlights the importance of continued research in the field of substance use, especially in periods of heightened vulnerability such as during the COVID-19 pandemic.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".