“I Can Do Anything if I’ve Overcome That”: A Collaborative Case Study of an Adolescent with Symptoms of Lyme Disease in Canada
Bibliographic record
Abstract
This qualitative case study explored the experiences of one Canadian adolescent with symptom-persistent Lyme disease. Lyme disease is the most prevalent vector-borne illness in North America, and infection rates are rising across Canada. Peak incidence occurs in children aged 5–9 years, making it a significant childhood infectious disease. This involves collaboration with an adolescent with symptom-persistent Lyme disease in Canada to address a gap in the literature. This empirical research was guided by the central research question: “What is the experience of an adolescent with symptom-persistent Lyme disease in Canada?” The purpose of this study was to understand the unique experiences of symptom-persistent Lyme disease in Canada by emphasizing one adolescent’s unique voice. The findings of this case study demonstrate the challenges this adolescent faced in receiving appropriate diagnosis and treatment for Lyme disease, pointing to a need for increased awareness among health professionals regarding the impact and prevalence of tick-borne illnesses for young people, their caregivers, and their healthcare providers. Additional findings suggest that collaborative healthcare may be beneficial for patients with symptom-persistent Lyme disease, and health researchers should continue to engage young people to ensure accurate representation of their experiences.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".