Colposcopy and Smoking Cessation: Survey and Education Intervention Pilot Study
Bibliographic record
Abstract
Objective: The objective of the study was to understand variations in smoking cessation practices across Canadian colposcopy clinics and to assess improvements in smoking cessation counseing after the provision of jurisdiction-specific resources. Methods: An electronic survey was sent to members of the Society of Canadian Colposcopists and the Gynaecologic Oncologists of Canada to characterize current smoking cessation counseling practices to inform the design of the educational initiative. Colposcopy clinics were invited to participate in the pilot involving the collection of smoking counseling data from patient charts pre- and postintervention. Region-specific smoking cessation resource toolkits were created for participating clinics. Descriptive statistics were used for the analysis. Results: 32/266 colposcopists responded to the survey. 25/32 respondents indicated that they asked all patients about their smoking status. Most respondents indicated that they tell patients smoking increases the risk of developing cervix cancer (88%) and that smoking negatively affects the immune system and human papillomavirus clearance (94%). Five clinics participated in the education initiative. While there is variation in practice, after providing smoking cessation resources to clinics, both assessing smoking status and smoking cessation counseling improved with most sites increasing their referral rates to family doctors or smoking cessation clinics (range: 10%–50%). Conclusions: Gaps exist in smoking cessation counseling in some Canadian colposcopy clinics. Standardizing assessment and documentation of smoking status, using effective models of counseling, and providing jurisdiction-specific resources to colposcopy clinics may improve smoking cessation counseling.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 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".