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Jeunes, Environnement Et Santé / Youth, Environment and Health (JES!-YEH!) Pilot Project in Four First Nation Communities in Quebec: Exposure Emerging Chemicals, Dietary Profiles and Health-Related Challenges

2018· article· en· W2989939770 on OpenAlexaffabout
Mélanie Lemire, Emad Tahir, Élyse Caron-Beaudoin, Elhadji Laouan Sidi, Michel Lucas, Communauté de Lac Simon, Community of Winneway - Long Point, Communauté de Nutashkuan, Communauté de Unamen Shipu, Nancy Gros-Louis Mc Hugh, Pierre Ayotte

Bibliographic record

VenueISEE Conference Abstracts · 2018
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsLibrary of ParliamentCentre hospitalier universitaire de QuébecCanadian Parks and Wilderness SocietyFirst Nations of Quebec and Labrador Health and Social Services CommissionUniversité du QuébecPolytechnique MontréalUniversité Laval
Fundersnot available
KeywordsEnvironmental healthIndigenousOvercrowdingPublic healthObesityGerontologyGeographyMedicineBiologyEcologyPolitical science

Abstract

fetched live from OpenAlex

The pilot project Jeunes, Environnement et Santé / Youth, Environment and Health (JES!-YEH!) was conducted in 2015 among children and youth (3-19y, n=198) in collaboration with four First Nation communities in Quebec. Main findings show very low exposure to traditional foods related contaminants (metals and older persistent organic contaminants). However, in some communities, elevated exposure to perfluorononanoic acid (PFNA), bisphenol A, diethylphosphate, monobenzylphtalate and 2,5-dichlorophenol, at higher levels than in the Canadian Health Measure Survey for the same age groups, was found. Food insecurity and overcrowding was important in some communities. Overall, ultra-processed foods and sweet beverages were frequently consumed, and a high prevalence of iron deficiency, anemia, elevated blood manganese and obesity was found. These findings underscore the importance of better understanding the determinants of healthy eating environments in indigenous communities.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.215
GPT teacher head0.326
Teacher spread0.111 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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