How to get the most out of the fit note
Bibliographic record
Abstract
PURPOSE We report on the effectiveness of the Ottawa Model for Smoking Cessation (OMSC), a multicomponent knowledge translation intervention, in increasing the rate at which primary care providers delivered smoking cessation interventions using the 3 A’s model—Ask, Advise, and Act, and examine clinic-, provider-and patient-level determinants of 3 A’s delivery. METHODS We examined the effect of the knowledge translation intervention in 32 primary care practices in Ontario, Canada, by assessing a cross-sectional sample of patients before the implementation of the OMSC and a second cross-sectional sample following implementation. We used 3-level modeling (clinic, clinician, patient) to examine the main effects and predictors of 3 A’s delivery. RESULTS Four hundred eighty-one primary care clinicians and more than 3,500 tobacco users contributed data to the evaluation. Rates of delivery of the 3 A’s increased significantly following program implementation (Ask: 55.3% vs 71.3%, P <.001; Advise: 45.5% vs 63.6%, P <.001; Act: 35.4% vs 54.4%, P <.001). The adjusted odds ratios (AOR) for the delivery of 3 A’s between the pre- and post-assessments were AOR = 1.94; (95% CI, 1.61–2.34) for Ask, AOR = 1.92; (95% CI, 1.60–2.29) for Advise, and AOR = 2.03; (95% CI, 1.71–2.42) for Act. The quality of program implementation and the reason for clinic visit were associated with increased rates of 3 A’s delivery. CONCLUSIONS Implementation of the OMSC was associated with increased rates of smoking cessation treatment delivery. High quality implementation of the OMSC program was associated with increased rates of 3 A’s delivery.
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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 | 0.006 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.228 | 0.124 |
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".