Changes in the reach of a smoking cessation program in Ontario, Canada, during the COVID-19 pandemic: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Given the harms associated with tobacco use, continuing the provision of smoking cessation treatment during the COVID-19 pandemic is critical. The aim of this study was to examine pandemic-related changes in enrolment, total treatment use and participant characteristics in a large, publicly funded smoking cessation program in Ontario, Canada. METHODS: We conducted a secondary data analysis of patients who enrolled in the program between Jan. 1, 2018, and Dec. 7, 2020. We used descriptive statistics to examine changes in treatment use. To test for differences in sociodemographic and health variables, we used segmented mixed-effects regression with a break point on Mar. 17, 2020, when Ontario declared a state of emergency. We tested 25 variables, using Holm's correction for multiplicity. RESULTS: We analyzed 60 373 enrolments. In the month after the break point, enrolments fell 69% and total visits fell 42% relative to previous years. After Mar. 17, 2020, those who enrolled were less likely to report employment in the previous week (absolute expected difference -12.4%, 95% confidence interval [CI] -15.0% to -9.8%); were more likely to be occasional (1.3%, 95% CI 0.6% to 1.9%) or noncurrent smokers (1.7%, 95% CI 0.8% to 2.6%); were less likely to have set a target quit date (-4.8%, 95% CI -7.0% to -2.6%); and were more likely to have a physical health (6.6%, 95% CI 4.0% to 9.2%), mental health (4.6%, 95% CI 1.9% to 7.2%) or substance use diagnosis (3.5%, 95% CI 1.3% to 5.6%). INTERPRETATION: Sharp decreases in new enrolments and subsequent visits to smoking cessation programs were seen when pandemic restrictions were implemented in Ontario, but the characteristics of the people who accessed the programs did not change markedly. Incorporating an equity perspective is essential when new models of care for smoking cessation are developed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".