Effects of COVID-19-Related Disruptions on Service Use in a Large Smoking Cessation Program
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic caused a rapid shift to virtual care, with largely unknown consequences for accessibility. The purpose of this study is to examine pandemic-related changes in use of care for smoking cessation. AIMS AND METHODS: We conducted a secondary analysis 65 565 enrollments in a large smoking cessation program in Ontario, Canada. We used piecewise mixed-effects regression to examine how weeks nicotine replacement therapy (NRT) received, as well as counseling provided and visits attended, varied with date of enrollment over three time periods: more than 6 months before the pandemic began; the 6 months before the pandemic; and the pandemic period itself. We then examined changes in the associations between use of care and participant characteristics by fitting a model including a set of interactions between time and other variables. Based on an omnibus test of these interactions, we then tested individual terms, using the Holm method to control the family-wise error rate. RESULTS: From the start of the pandemic in March 2020, the total weeks of NRT provided rose significantly and then declined, while the amount of counseling fell. Associations between NRT use and participant characteristics changed significantly after the pandemic onset. Individual models showed that people with lower income, living in areas of higher marginalization, unable to work, and reporting higher levels of depressive symptoms all received NRT for a longer time during the pandemic period. CONCLUSIONS: The pandemic led to small but significant changes in the amount of services used per enrollment. The transition to remote care appears to have reduced the effects of socioeconomic and health barriers. IMPLICATIONS: The amount of care used by participants in tobacco cessation treatment is known to be associated with health and sociodemographic characteristics. Most of these associations did not change markedly following the pandemic-related switch to virtual care in 2020; however, the effects of some economic and health barriers seem to have lessened, perhaps because of a likely reduction in transport and time requirements of treatment.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".