Tobacco use disorder treatment in primary care: implementing a clinical system pathway in Alberta.
Bibliographic record
Abstract
OBJECTIVE: To test a team-based, site-specific, multicomponent clinical system pathway designed for enhancing tobacco use disorder treatment by primary care physicians. DESIGN: A prospective cohort study. SETTING: Sixty primary care sites in Alberta. PARTICIPANTS: A convenience sample of 198 primary care physicians from the population of 2857. MAIN OUTCOME MEASURES: Data collection occurred between September 2010 and February 2012 on 3 distinct measures. Twenty-four weeks after the intervention, audits of the primary care practices assessed the adoption and sustainability of 10 tobacco clinical system pathway components, a survey measured changes in physicians' treatment intentions, and patient chart reviews examined changes in physicians' consistency with the treatment algorithm. RESULTS: The completion rate by physicians was 89.4%. An intention-to-treat approach was undertaken for statistical analysis. Intervention uptake was demonstrated by positive changes at 4 weeks in how many of the 10 clinical system measures were performed (mean [SD] = 4.22 [1.60] vs 8.57 [1.46]; P < .001). Physicians demonstrated significant favourable changes in 9 of the 12 measures of treatment intention (P < .05). The 18 282 chart reviews documented significant increases in 6 of the 8 algorithm components. CONCLUSION: Our findings suggest that the provision of a tobacco clinical system pathway that incorporates other members of the health care team and builds on existing office infrastructures will support positive and sustainable changes in tobacco use disorder treatment by physicians in primary care. This study reaffirms the substantive and important role of supporting how treatment is delivered in physicians' practices.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".