E cigarettes: Knowledge and attitudes in Canadian primary care physicians
Bibliographic record
Abstract
Electronic cigarettes(E cig) are being used for smoking cessation(SC), but with controversy about utility and safety. Physicians are discussing and advising E cig, but this has not yet been studied in Canada. An electronic study regarding physician attitude and experience regarding E cig was sent out to Canadian PCPs. 57 respondents, 68% male, 16% current or former smokers responded with 25% considering themselves knowledgeable or very knowledgeable about E cig. ∼75% of the physician9s patients had used E cig; less than half had discussed this with their physician. 7% said E cig are more effective than traditional SC strategies, 22% said less effective, 33% said non-comprable as they were so mechanistically different; but 38% said that they had insufficient knowledge. 58% agreed that E cig were safer than smoking, 8% less so, and 34% were neutral. ¼ felt that they should be used to assist SC, ¼ said they should not and ½ were undecided, despite an unidentified subsection of physicians(30%) who had personally tried an E cig which helped with their SC (67%). Specific legislation suggestions to improve the overall safe/appropriate use of E cigarettes included: no selling to minors (76%), no flavouring (53%), no advertising (51%), no nicotine (47%; current law in Canada); only 2% saying that no legislation was needed. Conclusion: E cigarettes can help the habit component of SC, especially if potential harm can be reduced. Concerns about E cig include the presence of carcinogens, being a gateway for children into tobacco ,and their use as a social phenomenon with secondary harm in even non-smokers. There is a gap in knowledge about this mode of therapy in primary care physicians that needs to be addressed.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 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".