Advice as a smoking cessation strategy: A systematic review and implications for physical therapists
Bibliographic record
Abstract
Although identified as a clinical priority, smoking cessation has been addressed minimally in the literature in the context of physical therapy practice. Smoking cessation advice delivered by a health professional can help smokers quit. The salient components of such advice however warranted elucidation to enable physical therapists to integrate this clinical competence into their practices. Therefore, we conducted a systematic review to elucidate the effectiveness of advice by a health professional and its components to optimize smoking cessation instituted in the context of physical therapy practice. Thirty source articles were identified. A random-effects model meta-analysis was used to assess the effectiveness of the advice parameters. Risk ratios (RRs) were used to estimate pooled treatment effects. RRs for brief, intermediate, and intensive advice were 1.74 (95% CI=1.37, 2.22), 1.71 (95% CI=1.39, 2.09), and 1.60 (95% CI=1.13, 2.27), respectively. Self-help materials, follow-up, and interventions based on psychological or motivational frameworks were particularly effective components of intermediate and intensive advice interventions. Advice can be readily integrated into physical therapy practice and used to initiate or support ongoing smoking cessation in clients irrespective of reason for referral. Incorporating smoking cessation as a physical therapy goal is consistent with the contemporary definition of the profession and the mandates of physical therapy professional associations to promote health and wellness, including smoking cessation for both primary health benefit and to minimize secondary effects (e.g., delayed healing and recovery, and medical and surgical complications). Thus, advice is an evidence-based strategy to effect smoking cessation that can be exploited in physical therapy practice. Further research to refine how best to assess smokers and, in turn, individualize brief smoking cessation advice could augment positive smoking cessation outcomes.
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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 | 0.021 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".