Advancing Supportive Cancer Care: A Survey of Naturopathic Doctors to Identify Practice Patterns, Knowledge Gaps and Resource Needs
Bibliographic record
Abstract
Background: Clinical guidance for naturopathic doctors (NDs) in supportive cancer care is limited, highlighting a potential need for resource development.Objectives: Describe naturopathic practice, identify oncology-related knowledge gaps, and determine preferred clinical resources. Methods: A 40-item online survey was distributed to NDs through naturopathic associations, social media, and informal networking. Questions varied based on whether respondents provided cancer care (“cancer stream”) or not (“general stream”). The survey ran from September 2023 to March 2024. Data analysis included frequency distributions and descriptive statistics. Results: Among 149 eligible responses, 62% practiced in Canada, 36% in the United States, and 2% elsewhere. The cancer stream (n = 99) primarily worked in community settings, offered hybrid care, and did not exclusively treat cancer patients. The largest knowledge gaps were related to intravenous (IV) green tea extract and curcumin, photodynamic and ozone therapy, managing tinnitus, and interactions between naturopathic interventions and stem cell transplants and photodynamic therapy. Time constraints were the main barrier to addressing knowledge gaps. The smallest gaps were reported for exercise counselling, the Mediterranean diet, IV vitamin C, vitamin/mineral infusions, and managing constipation, anxiety, diarrhea, fatigue, hot flashes, and depression. In total, 97% supported the development of clinical resources, with no format preference. In the general stream, 58% indicated that additional training would increase their likelihood of offering cancer care. Conclusion: This survey highlights oncology-related knowledge gaps, which were generally highest for less commonly used and studied therapies, and strong clinician support for resource development. Varied resource formats may accommodate different learning styles and improve dissemination.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".