Database Analysis of Depression and Anxiety in a Community Sample—Response to a Micronutrient Intervention
Bibliographic record
Abstract
Background: Depression and anxiety are common mental health concerns worldwide. Broad-spectrum multi-vitamin/mineral approaches have been found to alleviate a number of psychiatric symptoms. We investigated the effects of a nutrient intervention program, which includes optimizing vitamin D levels, on depression and anxiety outcomes from community-based program. Methods: We evaluated self-reported health measures of depression and anxiety collected as part of a community-based program focused on optimizing overall health through nutritional supplementation, education and lifestyle advice. Results: Data were collected from 16,020 participants, with measures including European Quality of Life Five Dimensions (EQ-5D) and Targeted Symptoms List (TSL) providing self-reported depression and anxiety. More than 56% of participants were identified as having elevated levels of depression and anxiety at baseline as reported on the EQ-5D. After one year in the program, 49.2% (n = 7878) of participants who reported any level of depression or anxiety at baseline reported improvement at follow-up. Of those who reported severe/extreme depression at baseline (n = 829), 97.2% reported improvement after one year. Regression analyses revealed a significant association of improvement in depression and anxiety with higher vitamin D status (>100 nmol/L) and more strenuous physical activity. Conclusion: Overall, people from the general population who suffer from mood and anxiety problems may benefit from improved nutritional status achieved with nutritional supplements.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".