Use of smoking cessation products: A survey of patients in community pharmacies
Bibliographic record
Abstract
OBJECTIVES: At 17.3%, smoking rates in Manitoba continue to exceed the national average. In this province, a total health care spending of more than $200 million per year has been attributed to smoking. This study examined the use of smoking cessation agents, including nicotine replacement products and prescription medications, in a sample of smokers in the city of Winnipeg. METHODS: A simple multiple-choice questionnaire was administered to willing individuals attending 2 community pharmacies in Winnipeg, Manitoba. Data on demographics, smoking habits, previous attempts of smoking cessation and previous and current use of over-the-counter and prescription smoking cessation products were collected anonymously. RESULTS: Of the 2237 individuals who were approached, 586 were smokers (26.2%) and 180 responded to the survey (30.7%); 48.9% were female. A majority of smokers (32.8%) reported smoking 16 to 25 cigarettes per day. More than 90% had smoked for more than 5 years, 27.2% had more than 5 previous quit attempts and 82.1% used smoking cessation products with the intention to quit. Self-motivation (44.4%) and family/friend advice (28.3%) were major reasons for quitting. Impact of health care practitioners' advice was low (6.4%). More than 80% of respondents reported that they had no insurance coverage for their smoking cessation products. Despite having the highest rate of use, both nicotine gum (33.3%) and patches (24.4%) were reported to have lower rates of perceived efficacy. Electronic cigarette (97.9%) and varenicline (70.6%) had the highest rates of reported effectiveness. CONCLUSION: Smokers wanting to quit undergo many attempts. Pharmacists should assume a key role in reaching out to smokers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".