Effective policy tools for tobacco control: Canadian public health practitioners’ perspectives
Bibliographic record
Abstract
This study canvased perspectives from public health practitioners who work in tobacco control in Canada to determine which policy tools or instruments – regulation, moral suasion, or taxation – or policy tool mixes are perceived to be the most effective in combatting tobacco consumption. Email interviews of public health practitioners (n = 11) were administered and thematically coded both deductively and inductively, as they possess a deeper, practical understanding of the nuances and feasibility of tobacco policy tool use. Four themes emerged: (1) No policy tool is perfect; (2) There is no standard measure of effectiveness; (3) Limitations in power to impact policy and technical knowledge exist, and; (4) The need for additional efforts with attention to cessation. The policy tools perceived as most effective were taxation, due to the focus on increased price, and regulation, due to the focus on restrictions on where smoking can take place. The findings suggest there is no standard for “effective” tobacco control policy and raise questions about how different public health practitioners determine what is effective, highlighting a need to determine indicators of effectiveness before inception of policy. Respondents tended to discuss limitations in power to impact policy and in their knowledge of evidence for policy tool effectiveness. Therefore, interventions that focus on knowledge translation may improve policy design and outcomes. There was a strong view that tobacco cessation work is incomplete.
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 | 0.090 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.039 | 0.030 |
| Scholarly communication | 0.024 | 0.008 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".