Address the gaps in tobacco cessation training and services: Developing professional organisational alliances to create social movements
Bibliographic record
Abstract
ISSUES: To contribute towards reversing the tobacco pandemic, professional organisational alliances must reduce the wide international variability in the smoking rates among health-care professionals and students, and also address the gaps in tobacco cessation training and services. APPROACH: Ongoing international surveys for monitoring smoking rates could provide the impetus for these alliances to develop programs that reduce smoking rates among professional and lay populations. KEY FINDINGS: Health professional organisations must advocate for systematically implementing comprehensive tobacco cessation training programs. IMPLICATIONS: These programs can include both evidence-based interventions and experience-based learning innovations. These innovations can help individuals address the limitations of evidence-based guidelines. This shift from teaching individuals about changing-specific risk behaviours to engaging individuals to learn how to change any risk behaviour expands the reach and impact of behaviour change programs. CONCLUSIONS: Practitioners and staff need first-hand experience of these learning innovations before guiding patients through the same process. Using both evidence-based guidelines and experience-based learning methods, organisational leaders can develop professional alliances to create social movements that promote healthy habits in general. For example, they can develop voluntary learning programs in primary care and community settings that are led by patients and that are for patients. Such bottom-up approaches have greater potential yield in addressing gaps in health promotion and disease prevention, and particularly for tobacco cessation services. This strategy is a more feasible option for resource-limited, developing countries that cannot afford costly tobacco cessation programs.
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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 | 0.033 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".