Ticks and lyme disease in natural areas: A segmentation analysis of visitor perceptions of risk and preferred communication strategies
Bibliographic record
Abstract
Reported Lyme disease cases have been rapidly increasing globally in recent years. Despite this, few studies have explored how the public perceives health risks associated with ticks and Lyme disease, especially within the context of visitor experiences in natural areas. To address this knowledge gap, we conducted a survey of visitors to one of Ontario, Canada’s most visited provincial parks. Despite high perceptions of risk, few visitors feel sufficiently educated about ticks and Lyme disease. Further, only 51% of visitors would return to the park if future regional incidents of tick-borne illnesses increased by 10–30% (under climate change). Interestingly, no significant correlations were uncovered between home distance and tick-related risk perceptions or self-reported tick education levels. These results may be indicative of the normalization of tick-related risk throughout many of southern Ontario’s most populated regions. Finally, our results also revealed that there is no preferred “one-size-fits-all” risk communication strategy that can be used to support risk-reducing behaviour change in a natural areas context. We recommend a diversity of evidence-based, on-site, and off-site strategies, that can be considered by governing authorities not only in Canada but indeed globally to reduce tick-related health and safety risks and ultimately safeguard visitor experiences in natural areas subject to increasing tick prevalence. Management Implications: The expanding range of ticks and increasing cases of Lyme disease are presenting significant risks to those who visit natural areas, requiring increasing management attention and action by governing authorities, including park managers and regional health units. Through a mixed-methods approach, we examined park visitor perceptions, behaviours, and preferred communication strategies regarding tick and Lyme disease risk. The results revealed a number of key findings, including: • While risk perceptions of tick bites are high, few visitors feel sufficiently educated about ticks and Lyme disease; • Only 51% of visitors would return to the park if future regional incidents of tick-borne illnesses increased by 10–30% (under climate change); • Visitors with further travel distances were significantly less likely to return to the park after experiencing an in-park tick bite; and, • There is no preferred “one-size-fits-all” risk communication strategy within a park visitor context.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".