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Record W2207974241 · doi:10.1177/1039856215608281

Mental health consequences of stress and trauma: allostatic load markers for practice and policy with a focus on Indigenous health

2015· article· en· W2207974241 on OpenAlexaff
Maximus Berger, Robert‐Paul Juster, Zóltan Sarnyai

Bibliographic record

VenueAustralasian Psychiatry · 2015
Typearticle
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsMcGill University
Fundersnot available
KeywordsAllostatic loadAllostasisMental healthComorbidityMental illnessMedicinePsychiatryIndigenousSocial deprivationPsychologyClinical psychologyGerontology

Abstract

fetched live from OpenAlex

OBJECTIVE: Mental health, well-being, and social life are intimately related as is evident from the higher incidence of psychiatric illness in individuals exposed to social stress and adversity. Several biological pathways linking social adversity to health outcomes are heavily investigated in the aims of facilitating early identification and prevention of adverse health outcomes. We provide a practice-orientated overview of the allostatic load model and how it relates to metabolic and cardiovascular comorbidity in psychiatric disorders. CONCLUSIONS: Allostatic load brings together a set of neuroendocrine, metabolic, immune and cardiovascular biomarkers that are elevated in individuals with adverse early life experiences and are predictive of cardiovascular and metabolic risk in psychiatric illness of critical importance for Indigenous Australians.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.056
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.031
GPT teacher head0.345
Teacher spread0.314 · 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 source (direct Gemma or distilled Codex), 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

Citations30
Published2015
Admission routes1
Has abstractyes

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