Nutrient- and non-nutrient-based natural health product (NHP) use in adults with mood disorders: prevalence, characteristics and potential for exposure to adverse events
Bibliographic record
Abstract
BACKGROUND: To address knowledge gaps regarding natural health product (NHP) usage in mental health populations, we examined their use in adults with mood disorders, and explored the potential for adverse events. METHODS: Food and NHP intake was obtained from 97 adults with mood disorders. NHP data was used to compare prevalence with population norms (British Columbia Nutrition Survey; BCNS). Bivariate and regression analyses examined factors associated with NHP use. Assessment of potential adverse effects of NHP use was based on comparing nutrient intakes from food plus supplements with the Dietary Reference Intakes and by reviewing databases for reported adverse health effects. RESULTS: Two-thirds (66%; 95% CI 56 to 75) were taking at least one NHP; 58% (95% CI 47 to 68) were taking NHPs in combination with psychiatric medications. The proportion of each type of NHP used was generally higher than the BCNS (range of p's < 0.05 to 0.0001). When intakes from food and NHP sources were combined, a small proportion exceeded any Lowest-Observed-Adverse-Effect-Levels: only for niacin (n = 17) and magnesium (n = 6), two nutrients for which the potential for adverse effects is minimal. Conversely, about 38% (95% CI 28 to 49) of the sample were taking a non-nutrient based NHP for which previous adverse events had been documented. CONCLUSIONS: The prevalent use of NHPs in this population suggests that health care providers need to be knowledgeable about their characteristics. The efficacy and safety of NHPs in relation to mental health warrants further investigation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".