Participatory Action Research on Canadians with Alternatively Diagnosed Lyme Disease: Feasibility Study
Bibliographic record
Abstract
This feasibility study provides background information on the problem of Lyme Disease (LD) underestimations in Canada, as well as the Federal Framework recommendation to collect human health data for Canadian residents “who do not meet the case definition for probable or confirmed LD, but who experience various symptoms consistent with LD or similar ailments.” This is followed by a summary of how the Public Health Agency of Canada (PHAC) assessed the agency’s progress in implementing the recommendation over a four-year period beginning in 2017 and ending in 2021. The study then provides a brief description of the mandate issued by PHAC to the consultant team by the end of 2021. It describes the methodology followed in assessing the feasibility of PHAC’s proposed initiative, which is to use a Participatory Action Research (PAR) approach to improve knowledge and inform future public health actions for Canadians diagnosed with Lyme disease (LD) via alternative means. The overall conclusions of the report are based on interviews with PHAC’S external partners and discussions of feasibility and success factors. They are organized by three sets of interview questions: project goals, rules of engagement, and data collection methods. The conclusion summarizes the study and offers suggestions on how to proceed with the project’s next phase.
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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 | 0.056 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.022 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".