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Record W2055911775 · doi:10.1007/s10903-013-9823-7

Acculturation and Nutritional Health of Immigrants in Canada: A Scoping Review

2013· review· en· W2055911775 on OpenAlexafffundabout
Dia Sanou, Erin K. O’Reilly, Ismael Ngnie‐Teta, Malek Batal, Nathalie Mondain, Caroline Andrew, K. Bruce Newbold, Ivy Lynn Bourgeault

Bibliographic record

VenueJournal of Immigrant and Minority Health · 2013
Typereview
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsMcMaster UniversityUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsImmigrationOutreachAcculturationPublic healthEthnic groupHealth promotionMedicineGerontologyHealth equityEnvironmental healthPolitical scienceNursing

Abstract

fetched live from OpenAlex

Although recent immigrants to Canada are healthier than Canadian born (i.e., the Healthy Immigrant Effect), they experience a deterioration in their health status which is partly due to transitions in dietary habits. Since pathways to these transitions are under-documented, this scoping review aims to identify knowledge gaps and research priorities related to immigrant nutritional health. A total of 49 articles were retrieved and reviewed using electronic databases and a stakeholder consultation was undertaken to consolidate findings. Overall, research tends to confirm the Healthy Immigrant Effect and suggests that significant knowledge gaps in nutritional health persist, thereby creating a barrier to the advancement of health promotion and the achievement of maximum health equity. Five research priorities were identified including (1) risks and benefits associated with traditional/ethnic foods; (2) access and outreach to immigrants; (3) mechanisms and coping strategies for food security; (4) mechanisms of food choice in immigrant families; and (5) health promotion strategies that work for immigrant populations.

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)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.506
Threshold uncertainty score0.983

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0110.017
Science and technology studies0.0020.001
Scholarly communication0.0040.001
Open science0.0020.001
Research integrity0.0020.001
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 head0.229
GPT teacher head0.492
Teacher spread0.263 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations243
Published2013
Admission routes3
Has abstractyes

Explore more

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