An evaluation of tick and Lyme disease information on health unit websites in Ontario
Bibliographic record
Abstract
Climate change has allowed for the expansion and intensification of blacklegged ticks; the vector of Lyme disease. Projections estimate that by the year 2049 all health units in Ontario will have suitable environmental conditions for the establishment of this vector. A review of website content from health units in Ontario was performed to assess the quality of tick and Lyme disease information provided to the public and health care providers. Websites were evaluated based on criteria such as the provision of Lyme disease information (i.e., transmission, symptoms, treatment, etc.), the inclusion of misleading or incorrect information, and visuals provided. The quality of textual and visual information varied substantially across the 35 health units analyzed. Eleven health units were found to provide misleading or incorrect information. Disparities were found between areas with current Lyme disease risk and those without. The majority of health units did not include satisfactory visual content pertaining to ticks. Given the expected expansion and intensification of blacklegged tick populations across the province, all health units must ensure the information communicated to the public about ticks and Lyme disease is of high-quality and consistent. We conclude with specific recommendations to improve the textual and visual content of websites.
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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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".