From Good to Great: The Role of Performance Coaching in Enhancing Tobacco-Dependence Treatment Rates
Bibliographic record
Abstract
PURPOSE The purpose of this study was to examine the incremental effect of performance coaching, delivered as part of a multicomponent intervention (Ottawa Model for Smoking Cessation [OMSC]), in increasing rates of tobacco-dependence treatment by primary care clinicians. METHODS In a cluster-randomized controlled trial, 15 primary care practices were randomly assigned to 1 of the following active-treatment conditions: OMSC or OMSC plus performance coaching (OMSC+). All practices received support to implement the OMSC. In addition, clinicians in the OMSC+ group participated in a 1.5-hour skills-based coaching session and received an individualized performance report. All clinicians and a cross-sectional sample of their patients were surveyed before and 4 months after introduction of the interventions. The primary outcome measure was rates of tobacco-dependence treatment strategy (Ask, Advise, Assist, Arrange) delivery. Secondary outcomes were patient quit attempts and smoking abstinence measured at 6 months’ follow-up. RESULTS Primary care clinicians (166) and patients (1,990) were enrolled in the trial. Clinicians in the OMSC+ group had statistically greater rates of delivery for Ask (adjusted odds ratio [AOR] = 1.69; 95% CI, 1.05-2.72), Assist (AOR = 1.64; 95% CI, 1.08-2.49), and Arrange (AOR = 2.01; 95% CI, 1.22-3.31). Sensitivity analysis found that the rate of delivery for Advise was greater only among those clinicians who attended the coaching session (AOR = 1.65; 95% CI, 1.10-2.49; P = .02). No differences were documented between groups for cessation outcomes. CONCLUSIONS Performance coaching significantly increased rates of tobacco-dependence treatment by primary care clinicians when delivered as part of a multicomponent intervention.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".