Educating smokers about the risk of blindness – insights to improve tobacco product health warning labels
Bibliographic record
Abstract
BACKGROUND: Health warning labels (HWL) on tobacco products help educate smokers about the health effects from smoking; however, there is a need to improve HWL content including images and text to increase effectiveness. In Canada, a HWL was created that communicates smoking's causal association with "blindness" from age-related macular degeneration (AMD). This study surveyed Canadian optometrists about their opinions regarding the image and text used in the "blindness" HWL. METHODS: An online survey was sent to all 4528 registered Canadian optometrists. Respondents were asked if the HWL conveyed important and believable information, and if the picture was appropriate. Optometrists were invited to make open-ended comments about the label which were analyzed using a qualitative analysis framework suitable for health policy evaluation. Frequency distributions were calculated for closed-ended questions. RESULTS: The survey was completed by 850 respondents (19 %). Most respondents (90 %) reported the message was believable/somewhat believable; while 35 % felt the picture was "too graphic". Some respondents reported in their open-ended comments that they were concerned the HWL was internally inconsistent because it reports there is "no effective treatment in most cases" for AMD but the image depicts someone undergoing surgery. There was concern that this may discourage patients from seeking needed treatment. CONCLUSION: The majority of Canadian optometrist respondents were in agreement that the new, "RISK OF BLINDNESS" pictorial HWL includes important, believable information. Some optometrists had concerns that the HWL included a confusing message or a message that may discourage some patients from pursuing treatment for AMD. Future development of blindness-related HWL should seek practitioner input.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".