Expanding the reach of the Quitline by engaging volunteers to market it in hospitals and shopping venues – a pilot study
Bibliographic record
Abstract
BACKGROUND: In Canada, although there are periodic media campaigns to raise awareness of Quitlines, these services are underused. We sought to determine if a dedicated kiosk, similar to that used in the retail industry but staffed by volunteers trained in smoking cessation techniques, would be effective method to enhance Quitline reach. METHODS: We located a kiosk in the foyer of two hospitals and in two shopping malls in Edmonton, Canada between Feb/2012 and July/2014. The cessation intervention was based on the 5 A's approach. Outcome was assessed by number of visits to the kiosk and referral rates to the Quitline. A cross sectional survey among small sample of visitors was used for evaluation. Descriptive statistics were used to summarize visitors' data. RESULTS: Of 1091 kiosk visitors, 53.3 % were current smokers, of whom 93.3 % indicated a willingness to quit. Of these, 32.1 % requested a Quitline referral at the time of the kiosk visit. Referral requests to the Quitline were greater when the kiosk was located in the non-hospital setting 39.1 % compared to 31.1 % in hospitals (P = 0.2). Referrals from the kiosk represented 6 % of total referrals received by the provincial Quitline during the study period. Following referral the Quitline was able to reach 50 % of those referred, of those, 17 % refused to proceed. At seven month follow up 30 day abstinence rate was 3.8 % of smokers who wished quit. Visitors agreed that the kiosk design was interesting (89.3 %) and increased their knowledge about tobacco and cessation options (88.8 %) and encouraged them to take action to quit (85.7 %). CONCLUSIONS: A "volunteer manned kiosk" can increase awareness of smoking cessation resources in the community and increase referral rates to Quitline services.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".