Approaches to Smoking Cessation
Bibliographic record
Abstract
Smoking cessation is a critical public health issue. Smoking cessation techniques are essential in reducing the burden of tobacco-related diseases and deaths. Medical practitioners have the potential to assist patients in quitting smoking, but deficits exist in the amount and type of training received in smoking cessation counseling. Smoking cessation is not a single event but a process that involves a change in a person's lifestyle, values, social circles, thinking and feeling patterns, and coping skills. Overcoming the hurdles associated with smoking cessation can increase an individual's self-efficacy in their ability to succeed at their quit attempt, which in turn acts to reduce the likelihood of a relapse and increase the likelihood of long-term sustained smoking cessation. Innovative techniques of treatment are necessary to provide effective smoking cessation intervention, especially for difficult cases. The use of pharmacotherapy, including nicotine replacement therapy and other medications, is an effective smoking cessation technique. Behavioral therapy is also a useful approach, including motivational interviewing, cognitive-behavioral therapy, and contingency management. The combination of pharmacotherapy and behavioral therapy can enhance the success rate of smoking cessation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".