The Effect of Natural Therapies in Combination with Usual Care for Depression: A Narrative Review
Bibliographic record
Abstract
Introduction: Major depressive disorder (MDD) is the most common and debilitating form of depression with a 12-month prevalence of 4.7% and a lifetime prevalence of 11.2% in Canada. Various classes of antidepressants are commonly used treatments for MDD; however, high failure rates occur due to adverse events and discontinuation of use. Non-drug and alternative interventions are commonly sought by people when drug treatment fails. The purpose of this investigation was to analyze the evidence on the effect of natural therapies in combination with pharmaceutical standard of care for the management of MDD Methods: The following inclusion criteria were defined before conducting the literature search: 1) population of adults with major depressive disorder, 2) intervention of lavender, folic acid or acupuncture, combined with standard treatment, 3) comparison group of a placebo, standard treatment or natural therapy used alone, 4) changes to Hamilton Depression Rating Scale (HAM-D) as the primary outcome. PubMed, APA PsycARTICLES and Google scholar were used for the research. The articles were limited to randomized clinical trials (RCTs), and systematic reviews with meta-analyses. The different therapies were used as key words in the literature search. Results: The literature search for ‘lavender’ yielded 214 studies, of which 3 RCTs met the criteria. ‘Folic acid’ yielded 680 studies of which 2 RCTs and 1 systematic review with meta-analysis met the criteria. ‘Acupuncture’ yielded 2240 studies of which 2 RCTs and 2 systematic reviews with meta-analyses met the criteria. Only the RCTs not summarized in the systematic reviews and meta-analyses were summarized in this review. Discussion: All ten studies using natural interventions showed a statistically significant decrease in the mean score change versus comparison groups, however, the magnitude of the effect varied between the studies. Sample sizes were small and there was significant heterogeneity between studies. Conclusion: Evidence suggests that natural therapies can be used adjunctively to the pharmaceutical care of MDD, however, the overall research quality is low and substantial heterogeneity exists between studies. Further, additional research using more rigorous methodologies and standardized interventions is needed.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".