Smoking cessation: barriers to success and readiness to change.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Smoking cessation interventions should be individualized based on patient history and readiness for change. The objective of this study was to assess stages of change and key components of smoking and cessation history among a sample of primary care patients. METHODS: A telephone survey of current or recent smokers identified smoking status, stage of change, motivation, concerns, relapse history, pharmacotherapy, and social support. RESULTS: Of 150 participants, most were within precontemplation (22.7 percent) or contemplation (44.0 percent) stages of change; 14.0 percent were in preparation, 4.7 percent in action, and 14.7 percent in maintenance. The primary motivation for quitting was to improve general health (42.3 percent). The most common cessation-related concerns were: breaking the habit, stress, and weight gain. Pharmacotherapy was discontinued due to adverse events in 31.5 percent of users. Intratreatment social support was reported by 17.5 percent. The most common reasons for relapse were falling back into the habit (36 percent), stressful situations (27 percent), and being around other smokers (25 percent). CONCLUSIONS: Targeted interventions are needed for patients in either precontemplation or contemplation stages. Counseling should focus on helping patients resolve barriers to cessation and reasons for relapse, particularly stress and weight management. Pharmacotherapy should be utilized when patients are ready to quit. Increased intratreatment social support and counseling appear warranted to support behavior change and appropriate medication use.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".