Improving smoking cessation support for Quebec’s smokers: an evaluation of Quebec’s telephone quitline
Bibliographic record
Abstract
INTRODUCTION: Quitlines are an important and widespread intervention that support smokers in their efforts to quit smoking and engage them into treatment services. Quebec's quitline, called "la ligne J'ARRÊTE", has been in operation since 2002. The objectives of this study were to evaluate treatment reach, provide a description of caller characteristics and to provide results on cessation outcome measures for Quebec's smoking cessation quitline. METHODS: We collected data at intake, assessing new caller volume, caller characteristics and treatment reach. We used a one-group quasi-experimental design to assess 30-day and six-month quit rates, at six-month follow-up. Intake data were collected for 1292 new quitline callers, 18 years of age and older, over a one-year period. RESULTS: Results indicated that the service reached 9 in 10 000 Quebec smokers. With respect to the total population of smokers in Quebec, the quitline reached proportionately higher numbers of smokers who were women, were 55 years of age and older and had a high school diploma or less. At follow-up, the 30-day point prevalence abstinence rate was 26.7%, while the six-month prolonged abstinence rate was 18.8%. CONCLUSION: These results indicate that the quitline contributed to helping callers quit smoking. They are in line with findings for other quitlines in Canada and the United States. However, quitline reach is comparatively limited, suggesting that additional investment in promotional efforts and research into ways of recruiting underserved populations into the service would increase public health impact.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".