Effect of COVID-19 on smoking cessation outcomes in a large primary care treatment programme: an observational study
Bibliographic record
Abstract
OBJECTIVES: The COVID-19 pandemic has changed patterns of smoking, other substance use and other health-related behaviours, leading to a virtualisation of non-urgent medical care. In this study, we examine associated changes in outcomes of smoking-cessation treatment. DESIGN: Observational study. SETTING: Data are drawn from 221 physician-led primary care practices participating in a smoking cessation program in Ontario, Canada. PARTICIPANTS: 43 509 patients (53% female), comprising 35 385 historical controls, 6109 people enrolled before the pandemic and followed up during it, and 1815 people enrolled after the pandemic began. INTERVENTION: Nicotine-replacement therapy with counselling. PRIMARY OUTCOME MEASURE: 7-day self-reported abstinence from cigarettes at a follow-up survey 6 months after entry. RESULTS: For people followed up in the 6 months (6M) after the pandemic began, quit probability declined with date of enrolment. Predicted probabilities were 31.2% (95% CI 30.0% to 32.5%) for people enrolled in smoking cessation treatment 6 months prior to the emergency declaration and followed up immediately after the state of emergency was declared, and 24.1% (95% CI 22.1% to 26.2%) for those enrolled in treatment immediately before the emergency declaration and followed up 6M later (difference=-6.5%, 95% CI -9.0% to -3.9%). Seasonality and total treatment use did not explain this decline. CONCLUSION: The probability of successful smoking cessation following treatment fell during the pandemic, with the decline consistent with an effect of 'exposure' to the pandemic-era environment. As many changes happened simultaneously, specific causes cannot be identified; however, the possibility that virtual care has been less effective than in-person treatment should be explored.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".