Delivering evidence-based smoking cessation treatment in primary care practice
Bibliographic record
Abstract
Objective To report on the delivery of evidence-based smoking cessation treatments (EBSCTs) within a sample of 40 Ontario family health teams (FHTs). Design In each FHT, consecutive patients were screened for smoking status and eligible patients completed a questionnaire immediately following their clinic visits (index visits). Multilevel analysis was used to examine FHT-level, provider-level, and patient-level predictors of EBSCT delivery. Setting Forty FHTs in Ontario. Participants Across the 40 participating FHTs, 24 033 patients were screened and 2501 eligible patients contributed data. Main outcome measures Provider performance in the delivery of EBSCTs during the preceding 12 months and during the index visits was assessed. Results The rate of provider delivery of EBSCT for the previous 12 months was 74.0% for the advise strategy. At the index visit, rates of EBSCT strategy delivery were 56.8% for ask ; 46.9% for advise ; 38.7% for assist ; 11.6% for prescribing pharmacotherapy; and 11.3% for arrange follow-up. Significant intra-FHT and intraprovider variability in the rates of EBSCT delivery was identified. Family health teams with a physician champion (odds ratio [OR] 2.0; 95% CI 1.1 to 3.6; P < .01) and providers who highly ranked the importance of smoking cessation (OR 1.7; 95% CI 1.1 to 2.7; P < .01) were more likely to deliver EBSCTs. Patient readiness to quit (OR 1.6; 95% CI 1.3 to 1.9; P < .001), presence of smoking-related illness (OR 1.6; 95% CI 1.2 to 2.1; P < .01), and presenting for an annual health examination (OR 2.0; 95% CI 1.6 to 2.5; P < .001) were associated with the delivery of EBSCTs. Conclusion Rates of smoking cessation advice were higher than previously reported for Canadian physicians; however, rates of assistance with quitting were lower. Future quality improvement initiatives should specifically target increasing the rates of screening and advising among low-performing FHTs and providers within FHTs, with a particular emphasis on doing so at all clinic appointments; and improving the rate at which assistance with quitting is delivered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".