Strategies to help patients stop smoking: the optometrist's perspective
Bibliographic record
Abstract
Ryan David Kennedy,1,2 Ornell Douglas2 1Department of Health, Behavior and Society, Institute for Global Tobacco Control, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; 2Propel Centre for Population Health Impact, University of Waterloo, Waterloo, ON, Canada Abstract: The following review article discusses tobacco's toll on individual and public health, and presents what is currently known about cigarette smoking's risk to ocular health. The article also discusses what eye care professionals – specifically optometrists – can do to help address tobacco use with their patients. Smoking is a leading preventable cause of age-related macular degeneration, and is also causally associated with the development of cataract, thyroid-associated ophthalmopathy, and uveitis. Smoking's causal association with vision loss is now used in some countries' health warning labels that appear on tobacco products and national social marketing materials including the US Centers for Disease Control and Prevention. Despite this uptake in health promotion education, very few eye care professionals regularly engage their patients in discussions about tobacco use. Optometrists can be a helpful addition to a smoking cessation health care network that already involves more than a dozen health care professions including medicine, nursing, pharmacy, dentistry, and dental hygiene. Optometrists can further play an important role in educating younger non-smoking patients about the risk of smoking and vision loss to support tobacco-use prevention. Optometrists report that they feel that addressing tobacco use is "not their job" or argue that this is more appropriately done by a family physician/general practitioner. This review article presents the rationale that all primary care providers have a role and a responsibility to discuss tobacco use with their patients. This review article outlines some techniques and strategies that optometrists can use to begin important discussions with their patients about tobacco use, to both support prevention and cessation efforts to support healthy ocular systems and improve public health. Further strategies that health care practitioners use to either connect patients with cessation services, or strategies that practitioners can deliver are also outlined. Keywords: smoking, tobacco use, prevention, cessation, age-related macular degeneration
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".