Is Stress Management Training a Useful Addition to Physician Advice and Nicotine Replacement Therapy during Smoking Cessation in Women? Results of a Randomized Trial
Bibliographic record
Abstract
PURPOSE: To determine whether a stress management (SM) program could improve cessation rates when added to usual care (UC) among women attempting to quit smoking. DESIGN: Randomized controlled trial conducted during a 12-month period. SETTING: Smoking cessation clinics located within two tertiary care centers in Ottawa, Ontario. SUBJECTS: A total of 332 women smokers 19 years or older who smoked 10 or more cigarettes per day were recruited via advertisements. INTERVENTION. Either UC (physician advice and nicotine replacement therapy) or UC plus an eight-session group SM training program (coping skills development relevant to smoking-specific and generic stressors). MEASURES: Point prevalence abstinence 2 and 12 months after study intake. A secondary outcome of interest was change in perceived stress during the intervention period. RESULTS: On an intent-to-treat basis, the addition of SM to UC had no incremental effect on 2- or 12-month abstinence rates. Abstinence rates at 2 months were 26.2% vs. 31.7% in the UC and SM groups, respectively (p = .59). At 12 months, the rates were 18.5% vs. 20.7% (p = .86). When quit rates were compared including only participants who demonstrated adequate adherence to the intervention protocol, there was a significant difference between the UC and SM groups at 2 months (34.9% vs. 48.7%; adjusted odds ratio, 1.88; 95% confidence interval, 1.04-3.42; p = .04) but not at 12 months (23.0% vs. 28.2%; adjusted odds ratio, 1.24; 95% confidence interval, .64-2.41; p = .53). There was a significant reduction in perceived stress from preintervention to postintervention; however, this decrease was not moderated by group assignment. CONCLUSION: The addition of SM in our setting neither increased abstinence rates nor reduced perceived stress over and above UC in women motivated to quit smoking. Poor attendance at the SM intervention undermined its effectiveness.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".