Exploring Visitor Perceptions and Behaviours Related to Ticks and Lyme Disease Risk in an Ontario Protected Area
Bibliographic record
Abstract
Lyme disease is the most common vector-borne zoonosis in North America and over the past decade, reported cases of the disease have been rapidly increasing in many regions throughout Canada. The relative novelty of this public health threat presents nature-based tourism and recreation organizations with a range of policy and management challenges. Currently, there is a limited understanding of public perceptions and behaviours associated with tick and Lyme disease risk, especially within a Canadian parks and protected areas visitation and visitor experience context. To address this practical and scholarly knowledge gap, this study utilized in-situ surveys to explore visitor perceptions, behaviours, and communication preferences related to tick and Lyme disease risk in one of Ontario’s most highly visited protected areas, Pinery Provincial Park.\nUsing a range of statistical and qualitative methods and analytical approaches, results suggest an increasing threat of ticks and tick-borne illnesses in Ontario and Canada more broadly will have significant implications for visitation and visitor experiences in parks. Despite visitors perceiving ticks as a significant in-park health risk, few visitors perceived themselves as sufficiently educated on ticks and tick-borne illnesses. Consequently, most visitors fail to adopt tick bite prevention behaviours both before and during their park visit. Further, it was revealed that a significant proportion of visitors will unlikely return to the park if regional cases of Lyme disease continue to rapidly increase under climate change.\nThese findings, combined with the latest projections on Canadian tick expansion, suggest That parks and other forms of protected areas in Ontario may experience significant decreases in visitation and, in turn, revenue, in the future. Considering these results, a variety of proactive management recommendations are discussed. Examples include the development and enhancement of on- and off-site education, communication, and outreach programmes where appropriate. Management agencies such as Ontario Parks should begin the difficult process of implementing proactive visitor risk management strategies to better ensure visitor health and safety both in the immediate term and in the emerging era of range expansion and increased human exposure to ticks and Lyme disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".