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Record W4408656721 · doi:10.54434/candj.196

Advancing Supportive Cancer Care: A Survey of Naturopathic Doctors to Identify Practice Patterns, Knowledge Gaps and Resource Needs

2025· article· en· W4408656721 on OpenAlexaffvenueabout
Erica Rizzolo, Mark Legacy, Ellen Conte, Mohamed El Sayed, Dugald Seely

Bibliographic record

VenueCAND Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsCanadian Institute for Health Information
Fundersnot available
KeywordsNaturopathyResource (disambiguation)MedicineNursingFamily medicineAlternative medicineComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.027
GPT teacher head0.421
Teacher spread0.394 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes3
Has abstractyes

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