From Prey to Hunter: Training Park Workers to Carry Out Tick Collection as an Alternative Approach for Lyme Disease Vector Surveillance
Bibliographic record
Abstract
CONTEXT Lyme disease (LD) has become a major public health concern in Canada, with flannel-dragging being the gold-standard approach for active tick surveillance. Limited resources hinders proper deployment of this surveillance in Quebec, prompting the exploration of alternatives. A participatory project was therefore developed to explore a new tick sampling scheme based on training of park workers. This training was first implemented in fall 2017 and is also scheduled for spring 2018. OBJECTIVESDevelop and validate a training targeting park employees to (1) allow them to carry out tick sampling activities autonomously and (2) inform them about LD prevention. INTERVENTIONTraining session was developed using governmental material. It combined a theoretical lecture covering basic concepts of LD and a practical segment in which workers carried out a first tick sampling under trainer’s supervision. Subsequently, participants were asked to carry an unsupervised sampling at the same location but on a different day. Ticks collected during samplings were identified and screened for pathogens. Evaluation of the training was done by phone interviews with park managers.OUTCOMEIn fall 2017, 64 workers were trained in 8 parks located in health regions with known LD endemic areas (Monteregie, Mauricie-Centre-du-Quebec, Estrie and Outaouais). All parks were sampled once during the training and half of them managed to carry independent samplings. Overall, 38 LD ticks vector were collected. High appreciation of the training and increased awareness of LD risk among workers were highlighted following evaluationCONCLUSIONAn innovative tick collection method involving workers was explored and will be validated in spring 2018. Such approach could potentially improve active tick surveillance activities while promoting occupational preventive measures. Similar initiatives could be implemented on a larger scale in the future to enhance LD surveillance in Canada.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".