Using the Ottawa Model for Smoking Cessation to Improve Tobacco Cessation Efforts in Transitional Care
Bibliographic record
Abstract
Tobacco cessation improves health and prevents death in patients who smoke. An evidence-based practice (EBP) project was implemented within an outpatient transitional care office to improve tobacco cessation efforts. The purpose of this doctoral project was to determine if the Ottawa Model for Smoking Cessation (OMSC) increased tobacco cessation counseling and referral rates. Counseling rates were defined as the percentage of smokers who received advice about quitting smoking. Referral rates represented the percentage of smokers referred to tobacco cessation services through the Maryland Quitline. The OMSC intervention emphasizes a three-step approach of ask, advise, and act to guide tobacco cessation assessment, counseling, and referral. Additional intervention components included outreach, training, electronic health record (EHR) enhancements, resource materials, and follow-up. The Stages of Change Model provided the theoretical framework for the project, and EBP implementation was guided by the Iowa Model. Participant data was collected from the EHR pre- and post-implementation with a total sample size of 248 participants, (n=125 pre; n=123 post). Data were analyzed using a z-test to compare the two groups’ mean counseling and referral rates, a t-test for equality of subgroups, and Chi-square test for analysis of other demographic characteristics. After implementation of the OMSC intervention, counseling rates increased by 22.1% (p = < .001) and referral rates increased by 6.5% (p < .002). Age group and race/ethnicity had a moderate association with referral rates (p < 0.05). Project findings provided support for this EBP change within the practice setting.
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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 | 0.023 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".