Evaluating rabies content on Ontario public health unit websites
Bibliographic record
Abstract
Rabies is a fatal zoonotic disease that is preventable through vaccines. Despite this, it remains a significant public health concern, highlighting the need for effective information dissemination. This study evaluated the availability, accuracy, and comprehensiveness of rabies-related content on the websites of 34 public health units (PHUs) in Ontario, Canada. To our knowledge, no prior research has examined these websites, making this study novel and urgently needed, especially considering the recent rabies case confirmed in Brantford-Brant on September 6, 2024. Key aspects of the disease, including transmission, symptoms, prevention strategies, high-risk populations, and rabies post-exposure prophylaxis (RPEP) were assessed. Findings revealed, while most PHUs provided basic information, there are noteworthy gaps regarding high-risk populations and clear RPEP guidelines. Out of the 34 PHUs reviewed, inconsistencies were noted, with 17.7% of websites containing incorrect information, 26.5% presenting misleading content, 67.7% offering incomplete information, and 17.7% providing outdated data. These communication gaps could undermine rabies prevention efforts in Ontario. Standardizing information across PHU websites is vital to improving public understanding, ensuring effective disease management, and enhancing health care providers’ capacity to deliver timely advice. Addressing these issues will help to strengthen public health outcomes and build trust in public health authorities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; both teacher heads agree on what is shown here.
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".