<i>On the Front Line of Smoking Cessation:</i> Education Needs of Community Pharmacists
Bibliographic record
Abstract
Background: Nicotine replacement therapy (NRT) is now available without prescription, and pharmacists have an opportunity to be the primary health care professionals advising patients who want to try this smoking cessation aid. Undergraduate pharmacy education in Canada incorporates some tobacco-related training, and continuing education opportunities are available. However, there are no published reports of the self-reported educational needs of a broad sample of practising Canadian community pharmacists with regard to smoking cessation. Methods: Practising community pharmacists in four Canadian provinces were surveyed about their tobacco-related education and knowledge, practice environment, practices related to helping people quit smoking, needs for further information and training, and methods by which this information might be delivered. Results: A corrected response rate of 72% was obtained, giving a weighted n of 960 responses. Most Canadian community pharmacists reported that they would find it helpful to receive additional information or training on a wide range of smoking cessation topics, such as behavioural counselling techniques and motivating patients to quit. Pharmacists who reported that they were familiar with aids such as Helping Your Patients Quit Smoking: A Cessation Guide for Pharmacists (Canadian Pharmacists Association) and who reported that the guide had led them to make changes in their practice were more likely to report that additional information or training would be helpful. Pharmacists who perceived their tobacco-related clinical and non-clinical roles to be important were more likely to report that additional training would be helpful than were those who perceived these roles as less important. Conclusions: Although most Canadian community pharmacists would find it helpful to receive additional smoking-related information and training, those who perceive this to be an important activity and who are already using available materials showed the most interest. The challenge to Canadian pharmacy educators is to encourage the enthusiasm of those already interested in helping their patients to quit smoking, while raising the level of interest and motivation among all community pharmacists.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".