Predispose, precipitate, perpetuate, and protect: how diet and the gut influence mental health in emerging adulthood
Bibliographic record
Abstract
factors to identify salient influences on illness states, and to help guide patient care. Mental illness is a significant cause of morbidity and mortality worldwide. Mental health is a complex combination of biological, psychological, environmental, and social factors. There is growing interest in the gut-brain-microbiome (GBM) axis and its impact on mental health. We use the medical model of the 4Ps to explore factors involving the connection between nutrition and the GBM axis and their associated risks with mental health problems in emerging adults (EAs), a life stage when mental illness onset is the most common. We review the impact of current dietary trends on the GBM and on mental health, and the role that gut microbiome-based interventions can have in modulating the GBM axis of EAs. We discuss the implications of gut health on the GBM and areas for clinical intervention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".