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Record W2763496149 · doi:10.3390/nu9101116

Serum 25-Hydroxyvitamin D Concentrations and Indicators of Mental Health: An Analysis of the Canadian Health Measures Survey

2017· article· en· W2763496149 on OpenAlexaffabout
F.I. Chu, Arto Öhinmaa, Scott Klarenbach, Zing-Wae Wong, Paul J. Veugelers

Bibliographic record

VenueNutrients · 2017
Typearticle
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMental healthVitamin D and neurologyLogistic regressionEnvironmental healthDepression (economics)MedicineCausality (physics)AnxietyHealth indicatorDemographyPsychiatryInternal medicinePopulation

Abstract

fetched live from OpenAlex

The main function of vitamin D is calcium homeostasis. However, emerging evidence has correlated adequate serum 25-hydroxyvitamin D (25(OH)D) concentrations with better mental health. The objective of this study is to investigate the association of serum 25(OH)D concentrations with indicators of mental health such as depression, anxiety, and stress. Associations of serum 25(OH)D concentrations with four indicators of mental health were examined using ordered logistic regression models with increasing specificity that account for demographics, socio-economic status, and health. Margin effects are used to determine the probability of the average adult Canadian being in the best mental health state by groupings of serum 25(OH)D concentrations. A robust association between serum 25(OH)D concentrations and the indicators of mental health were observed. In the fully adjusted ordered logistic model, an average Canadian appeared more likely to experience better mental health when serum 25(OH)D concentrations were higher. This study adds to the weight of the existence of an association between vitamin D status and mental health, but, as this study is cross sectional, it does not establish causality. Due to the low risk of harm from toxicity and the relative modest costs of vitamin D supplements, more research to establish the effectiveness and causality of this relationship is recommended.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score0.772

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.052
GPT teacher head0.370
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations34
Published2017
Admission routes2
Has abstractyes

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