Evaluation of a community-based <i>One Health</i> intervention to reduce the risk of Lyme disease in a high-incidence municipality
Bibliographic record
Abstract
Abstract Integrated interventions coherent with the One Health approach are required to maximize the effectiveness of tick-borne disease prevention. The objective of this study was to evaluate the feasibility of a community-based One Health preventive intervention in a municipality reporting a high incidence of Lyme disease (LD) in the province of Quebec, Canada. The intervention integrated several activities to promote the adoption of preventive behaviours in the community (community component), with the reduction of infected ticks in the environment (environmental component). To evaluate short-term effects of the community component, quantitative data on knowledge, attitudes, and practices (KAP) of citizens were collected using online questionnaires at the beginning of the intervention and 1 year later. To evaluate the implementation of the intervention, a qualitative approach was principally used, and individual interviews (n=37) were conducted with key informants. Results showed that having a high level of participation in the community activities included in the intervention was associated with a higher adoption of preventive measures at the end of the intervention, although participation was not significantly associated with changes in KAP over the intervention period. Interviews revealed that the community mobilization approach was perceived as an effective and sustainable way to empower citizens and researchers with regard to LD prevention. This study suggests that community-based interventions of this type offer a promising approach to the prevention of tick-borne diseases.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".