Adjunctive Naturopathic Medicine in the Management of Depression and Anxiety Symptoms: A Retrospective Chart Review
Bibliographic record
Abstract
Background: Naturopathic medicine uses natural and evidence-based treatments to promote health. The objective of this study was to characterize and evaluate the effectiveness of naturopathic medicine in reducing symptoms of anxiety and depression in patients seeking care from the Mental Health Shift at a Canadian naturopathic teaching clinic. Methods: Charts of 192 consecutive patients who attended appointments between 1 January 2019 and 6 March 2020 were reviewed. Subjects were included if they screened positive on the General Anxiety Disorder 7 (GAD7) or the Patient Health Questionnaire 9 (PHQ9) and attended follow up at least 4 weeks after initial treatment. Results: Of the 22 included subjects, a clinically significant decrease of at least 5 points occurred in 76% of individuals with elevated baseline PHQ9 scores and 59% of individuals with elevated GAD7 scores. Mean PHQ9 and GAD7 scores decreased 7.5 (p<0.0001) and 4.6 (p<0.008) points, respectively. Mild adverse events were reported in 9 charts (41%). No serious adverse events were reported. Most subjects were also in the care of a medical doctor and counsellor. Conclusion: Naturopathic medicine as performed on the Mental Health Shift may be effective in reducing depression and anxiety symptoms as an adjunctive treatment. Further research incorporating comparison groups is warranted.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".