Injecting doubt: responding to the naturopathic anti-vaccination rhetoric
Bibliographic record
Abstract
There is growing controversy about vaccination rates in Canada. A significant percentage of the population is uncertain about the science of vaccines, and in some areas ‘herd immunity’ is being threatened. Hesitancy to vaccinate is a complex phenomenon, but there is little doubt that complementary and alternative medicine (CAM) providers have played a role. In this study, our first objective was to examine websites of naturopathic clinics and practitioners in the provinces of British Columbia and Alberta, looking for (1) the presence of discourse that may contribute to vaccine hesitancy, and (2) recommendations for ‘alternatives’ to vaccines or flu shots. Of the 330 naturopath websites we analysed, 40 included vaccine hesitancy discourse and 26 offered vaccine or flu shot alternatives. Using these data, we explored the potential impact such statements could have on the phenomenon of vaccine hesitancy. Our second objective was to consider these misrepresentations in the context of Canadian law and policy, and to outline various legal methods of addressing them. We concluded that tightening advertising law, reducing CAM practitioners’ ability to self-regulate, and improving enforcement of existing common and criminal law standards would help limit naturopaths’ ability to spread inaccurate and science-free anti-vaccination and vaccine-hesitant perspectives.
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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 | 0.019 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.036 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| 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".