A Comparison of Quit Outcomes for Men and Women in a Smoking Cessation Program Offering Personalized Nicotine Replacement Therapy and Counseling in Primary Care Clinics
Bibliographic record
Abstract
INTRODUCTION: Women may have greater difficulty achieving long-term abstinence following a quit attempt compared to men. We sought to determine whether there were differences in treatment characteristics or outcome between female and male primary care patients enrolled in a smoking cessation program providing personalized nicotine replacement therapy (NRT) with counseling support. AIMS AND METHODS: The sample included 27 601 Ontarians (53% female, 47% male) who enrolled in the Smoking Treatment for Ontario Patients program between 2016 and 2018. Dose, type, and duration of NRT supplied were personalized to need. Thirty-day point prevalence tobacco smoking abstinence was self-reported via online or telephone survey at 6 months after enrollment. RESULTS: Both female and male participants received a median of 8 weeks of NRT. Types of NRT received were similar, with 80% of both female and male participants receiving patch and short-acting NRT. The total cumulative dose was somewhat higher for men (1373 mg vs. 1265 mg, p < .001); but when calculated as dose per day, per cigarette smoked at baseline, the dose was slightly higher among women (1.6 mg vs. 1.5 mg, p < .001). Quit rates at 6 months were lower for women versus men (24% vs. 27%; adjusted OR = 0.84 [95% CI = 0.78 to 0.90], p < .001). There were no significant interactions between gender and dose, type, or duration of NRT supplied. CONCLUSIONS: Women were slightly less likely to quit than men, despite receiving similar treatment. There was no evidence that women benefitted more or less from variations in dose, type, or duration of NRT supplied. IMPLICATIONS: Women who received personalized NRT with counseling support in a primary care setting had lower quit success than men. Although the absolute difference in quit outcome was small, the public health impact may be large given the burden of disease associated with continued smoking. NRT supplied was similar for women and men, and there was no evidence upon which to suggest tailoring the dose, duration, or type, based on gender alone. Further research to identify the underlying biological and social factors responsible for reduced quit success will help identify ways to optimize treatment for women.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".