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Record W2330847472 · doi:10.13172/2053-2636-1-1-572

Adverse effects of pollution on mental health: the stress hypothesis

2013· article· en· W2330847472 on OpenAlexaboutno aff
Dave Lanoix, Pierrich Plusquellec

Bibliographic record

VenueOA Evidence-Based Medicine · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthPsychologyPollutionEnvironmental healthStress (linguistics)MedicinePsychiatryBiology

Abstract

fetched live from OpenAlex

Introduction Environmental contaminants are ubiquitous. Among the most studied environmental contaminants, lead, mercury, polychlorinated biphenyls and pesticides have been found to impact mental health. In particular, exposure to environmental contaminants has been related to executive functions and emotional/ behavioural maladjustment in children, and cognitive variability in elders. We hypothesise that the association between environmental contaminants and mental health, particularly in children and elders, could be explained by a disruption of the stress system. Discussion Environmental contaminants act at very low levels of exposure that are not reflected by high doses. Therefore, public regulation agencies are consistently reducing acceptable blood levels of exposure. For example, clinical management plans for children and elders at risk of toxicity are thus adapted to reflect the novel blood lead action level. Environmental contaminants acting at such low doses were found to be endocrinedisrupting chemicals, and most studies have thus focused on the sexual steroids system. However, the stress system, which produces glucocorticoids, has been overlooked. *Corresponding author Email: pierrich.plusquellec@umontreal.ca 1 Centre for Studies on Human Stress, Department 226, 7401 Hochelaga, Montreal, H1N3M5 Quebec, Canada 2 School of Psychoeducation, University of Montreal, Montreal, Quebec, Canada

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
gptno category
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
models agreeAgreement compares identical category sets and study designs across arms.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.029
GPT teacher head0.259
Teacher spread0.231 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical · Other

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

Citations8
Published2013
Admission routes1
Has abstractyes

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