The Stop Smoking Before Surgery Program
Bibliographic record
Abstract
OBJECTIVE: This study aimed to examine the impact of a Stop Smoking Before Surgery (SSBS) program in a health authority where responsibility for surgical services is shared by health professionals in regional centers and outlying communities. METHODS: A between-subjects, pre-post mixed method program evaluation was conducted. Elective surgery patients at 2 Northern Canadian hospitals were recruited and surveyed at 2 time points: pre-SSBS implementation (n = 150) and 1 year post-SSBS implementation (n = 90). In addition, semistructured interviews were conducted with a purposeful sample of participants (n = 18). RESULTS: Participants who received information about stopping smoking before surgery post-SSBS implementation were more likely than expected to have reduced their smoking, χ(2)(1, 89) = 10.62, P = .001, and had a significantly higher Awareness of Smoking-Related Perioperative Complications score than those that were advised to quit smoking prior to SSBS implementation (U = 1288.0, P < .001). Being advised by a health care professional was the second strongest predictor of whether or not participants reduced their smoking before surgery post-SSBS implementation. However, there was no significant change in the number of participants who reported being advised to quit smoking before surgery between groups. CONCLUSION: Providing surgery-specific resources to increase awareness of and support for surgery-specific smoking cessation had limited success in this rural context. Additional strategies are needed to ensure that every surgical patient who smokes receives information about the benefits of quitting for surgery and is aware of available cessation resources.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".