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Record W4376849912 · doi:10.32920/cd.v6i3.1522

Government-Assisted Syrian Refugees' Perceptions of Dietary Acculturation in Canada

2023· article· en· W4376849912 on OpenAlexvenueaboutno aff
Rand AL-Rajie

Bibliographic record

VenueJournal of Critical Dietetics · 2023
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
Fundersnot available
KeywordsAcculturationThematic analysisSnowball samplingRefugeePopulationGovernment (linguistics)PsychologyImmigrationQualitative researchGerontologySocial psychologyMedicineEnvironmental healthPolitical scienceSociologySocial science

Abstract

fetched live from OpenAlex

Diet acculturation is a complicated process where refugees must discover new ways of consuming traditional foods, adopt new foods, or eliminate other foods from their diets in a host country. The purpose of this phenomenological case study was to explore the ways in which Syrian refugees describe diet acculturation since their arrival to a city in Southern Ontario. Individuals eligible to participate were government-assisted Syrian refugees who were at least 18 years old. Purposive and snowball sampling methods were used to recruit participants. Researcher-constructed, semi-structured, face-to-face interviews were used to collect data in Arabic. Data were recorded and transcribed verbatim in Arabic and then translated to English. Inductive coding, second-cycle coding and thematic analysis were used to analyze the data. Using the dietary acculturation model as a lens, the study found participants sought to maintain a connection to their Syrian identity through food, yet food prices, access, and quality and perceptions of health, along with children’s peer influence resulting in bicultural eating patterns impacted the participants’ experiences with dietary acculturation. The results of the study will assist food and nutrition professionals in understanding dietary acculturation in the Syrian refugee population.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.398
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.001
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 head0.144
GPT teacher head0.469
Teacher spread0.325 · 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
Published2023
Admission routes2
Has abstractyes

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