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Record W7100434647

RESEARCH Open Access Perceived barriers in accessing food among recent Latin American immigrants in Toronto

2016· article· en· W7100434647 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
Fundersnot available
KeywordsLatin AmericansImmigrationThematic analysisLanguage barrierFood insecurityWork (physics)Sample (material)Food securityCultural diversity
DOInot available

Abstract

fetched live from OpenAlex

recent immigrant households experience more food insecurity than the general population, but limited information is available about the personal, cultural, and social factors that contribute to this vulnerability. This study focused on recent Latin American (LA) immigrants to explore their perceived barriers in acquiring safe, nutritious, and culturally-appropriate food. Design: A cross-sectional mixed-method design was applied to collect information from a convenience sample of 70 adult Spanish/Portuguese speakers who had arrived in Toronto within the last five years. Face-to-face interviews were conducted with primary household caregivers to obtain responses about barriers to acquiring food for their households; data were analyzed using a thematic analysis technique. Results: Four main categories of barriers were identified: limited financial resources; language difficulty; cultural food preferences; and poor knowledge of available community-based food resources and services. Inadequate income was the main impediment in accessing adequate food, and was related to affordability of food items, accessibility of food outlets and transportation cost, and limited time for grocery shopping due to work conditions. Language barriers affected participants ’ ability to obtain well-paid employment and their awareness about and access to available community-based food resources. Cultural barriers were related to food preferences and limited access to culturally-appropriate foods and resources.

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: yes
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Observationalhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.001
metaresearch head score (Gemma)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score0.812

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.415
GPT teacher head0.598
Teacher spread0.184 · 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 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
Published2016
Admission routes1
Has abstractyes

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