Lyme Disease Training and Knowledge Translation Resources Available to Canadian Healthcare Professionals: A Gray Literature Review
Bibliographic record
Abstract
INTRODUCTION: Lyme Disease (LD) is the most common tick-borne disease in North America. With the number of cases increasing yearly, Canadian healthcare professionals (HCP) rely on up-to-date and evidence-informed guidelines, instruction, and resources to effectively prevent, diagnose, and treat Lyme disease (LD). This review is the first of its kind to examine gray literature and analyze the diversity of recommendations provided to Canadian HCP about the prevention, diagnosis, and treatment of Lyme disease. METHODS: A gray literature review consisting of 4 search strategies was conducted to retrieve materials targeted to Canadian HCP. Searches within targeted websites, targeted Google searches, and gray literature databases, and consultation with content experts were done to look for continuing medical education (CME) events, clinical flow charts, webinars, videos, and reference documents that discussed the prevention, diagnosis, and treatment of Lyme disease. RESULTS: A total of 115 resources were included in this study. Recommendations surrounding prevention strategies were less varied between materials, whereas diagnosis and treatment recommendations were more varied. Our findings suggest that Canadian HCP are met with varying and sometimes contradictory recommendations for diagnosing and treating LD. CONCLUSIONS: Due to the increasing incidence of LD in Canada, there is a greater need for resource consistency. Providing this consistency may help mitigate LD burden, standardize approaches to prevention, diagnosis and treatment, and improve patient outcomes.
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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 | 0.012 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.028 | 0.042 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".