How to get the most out of the fit note
Bibliographic record
Abstract
<h3>PURPOSE</h3> 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—<i>Ask</i>, <i>Advise</i>, and <i>Act</i>, and examine clinic-, provider-and patient-level determinants of 3 A’s delivery. <h3>METHODS</h3> 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. <h3>RESULTS</h3> 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 (<i>Ask</i>: 55.3% vs 71.3%, <i>P</i> <.001; <i>Advise</i>: 45.5% vs 63.6%, <i>P</i> <.001; <i>Act</i>: 35.4% vs 54.4%, <i>P</i> <.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 <i>Ask</i>, AOR = 1.92; (95% CI, 1.60–2.29) for <i>Advise</i>, and AOR = 2.03; (95% CI, 1.71–2.42) for <i>Act</i>. The quality of program implementation and the reason for clinic visit were associated with increased rates of 3 A’s delivery. <h3>CONCLUSIONS</h3> 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".