Do smokers know how to quit? Knowledge and perceived effectiveness of cessation assistance as predictors of cessation behaviour
Bibliographic record
Abstract
AIMS: Despite the existence of effective cessation methods, the vast majority of smokers attempt to quit on their own. To date, there is little evidence to explain the low adoption rates for effective forms of cessation assistance, including pharmaceutical aids. This study sought to assess smokers' awareness and perceived effectiveness of cessation methods and to examine the relationship of this knowledge to cessation behaviour. DESIGN: A random-digit-dial telephone survey (response rate = 76%) with 3-month follow-up was conducted with 616 adult daily smokers in South-Western Ontario, Canada. MEASUREMENTS: A baseline survey assessed smoking behaviour, as well as smokers' awareness and perceived effectiveness of cessation assistance. A follow-up survey measured changes in smoking behaviour and adoption of cessation assistance at 3 months. FINDINGS: Participants demonstrated a poor recall of cessation methods: 45% of participants did not recall nicotine gum, 33% did not recall the nicotine patch and 57% did not recall bupropion. Also, many participants did not believe that the following cessation methods would increase their likelihood of quitting: nicotine replacement therapies (36%), bupropion (35%), counselling from a health professional (66%) and group counselling/quit programmes (50%). In addition, 78% of smokers indicated that they were just as likely to quit on their own as they were with assistance. Most important, participants who perceived cessation methods to be effective at baseline, were more likely to intend to quit (OR = 1.80, 95% CI: 1.12-2.90), make a quit attempt at follow-up (OR = 1.80, 95% CI: 1.03-3.16) and to adopt cessation assistance when doing so (OR = 3.62, 95% CI: 1.04-12.58). CONCLUSIONS: This research suggests that many smokers may be unaware of effective cessation methods and most underestimate their benefit. Further, this lack of knowledge may represent a significant barrier to treatment adoption.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".