“If I'm a naturopath, It's because I trust nature above everything else”: Canadian naturopaths' construction of vaccination as a risk object
Bibliographic record
Abstract
Canadians and Quebecers increasingly consult complementary and alternative medicine (CAM) practitioners in parallel with biomedical providers. The close relationship between vaccine hesitancy and CAM use remains under explored in Western countries. We present the results of a qualitative study conducted among one of Quebec's most used CAM approaches: naturopathy. Using Boholm and Corvellec's relational theory of risk to illustrate naturopaths' construction of vaccination as an “object of risk”, we describe how the health representations of 30 Quebec naturopath interviewees are associated with the ways they perceived the risks of infectious diseases and vaccination. Our findings illustrate how Quebec naturopaths' view the body as “at risk” from the possible harmful effects of vaccines. For these naturopaths, the body is a site, a “terrain”, where homeostasis must continually be preserved, and needs to be protected from risks such as vaccines—which were seen as far riskier than infectious diseases—through natural means. Such views are often perceived as unscientific or even irrational by public health researchers. Our study highlights that naturopaths' attitudes towards vaccination are perfectly aligned with the epistemological tenets of their risk representations and conceptions of health.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.017 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".