Patient Tobacco Use in Optometric Practice
Bibliographic record
Abstract
PURPOSE: A national census survey of optometrists in Canada measured knowledge of ocular diseases associated with smoking cigarettes and current practice behaviors related to addressing tobacco use with patients, including prevention and cessation. Optometrists were also asked to identify tools to assist addressing tobacco use with patients. METHODS: An online bilingual (English/French) survey was developed and an e-mail with a link to the survey was sent to all 4528 optometrists registered in Canada. No participation incentives were provided. Frequency data were tabulated for survey items. Logistic regression models were fit to understand respondent characteristics associated with discussing tobacco use prevention and cessation with patients. RESULTS: The response rate was 19% (850 responses). Almost all respondents (98%) believed that smoking cigarettes was a risk factor for developing age-related macular degeneration; approximately half (55%) assessed the smoking status of patients during their initial visit; 7% reported that they discussed the benefits of tobacco use prevention with patients younger than 19 years; and 33% reported that they always or regularly assess their patients' interest in quitting smoking. Respondents who completed the survey in English were more likely (odds ratio, 2.4; 95% confidence interval, 1.01 to 5.65) to deliver prevention messaging, compared with respondents who completed the survey in French. Male respondents were less likely to assess patients' interest in quitting (odds ratio, 0.7; 95% confidence interval, 0.50 to 0.97) than female respondents. Most respondents (90%) were interested in a continuing education program about the impact of smoking on vision and eye health as well as strategies for discussing tobacco cessation and prevention. CONCLUSIONS: Optometrists are aware of the impact of smoking on ocular health; however, most respondents do not systematically engage in tobacco use prevention and cessation practices. Providing optometrists with tools, including continuing education, may help support patient conversations about the risks of tobacco use and improve public health.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".