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Record W2125127036 · doi:10.1002/eat.22295

Body image dissatisfaction among immigrant children and adolescents in Canada and the United States: A scoping review

2014· review· en· W2125127036 on OpenAlexaffabout
Melissa Kimber, Jennifer Couturier, Katholiki Georgiades, Olive Wahoush, Susan M. Jack

Bibliographic record

VenueInternational Journal of Eating Disorders · 2014
Typereview
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAcculturationImmigrationThematic analysisPsychologyGerontologyMedicineDevelopmental psychologyQualitative researchPolitical scienceSociology

Abstract

fetched live from OpenAlex

OBJECTIVE: To systematically summarize the literature examining body image dissatisfaction (BID) among immigrant children and adolescents living in Canada and the United States (US). METHOD: Sources were identified by entering search terms into six electronic databases and by completing an electronic hand search of research journals focusing on body image. Eligible sources were those published between 1946 and November 2012, conducted within Canada or the US, included immigrant children or adolescents (<18 years), and measured BID through self-report. Synthesis followed the principles of thematic and content analysis (Vaismoradi et al., Nurs Health Sci, 2013,15,398-405). RESULTS: A total of 12 sources were included in our synthesis, spanning years 1991 to 2010. These studies indicate that immigrant children and adolescents experience BID. However, the literature is plagued by a disproportionate focus on females, Latino/Hispanic immigrants, and inadequate attention to issues of measurement. DISCUSSION: There is no evidence about the BID experiences of immigrant children and adolescents in Canada and limited information has stemmed from the US. A more robust evidence-base should include the use of advanced methods to examine the influence of acculturation and acculturative stress on BID among immigrant male and female children and adolescents.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.799
Threshold uncertainty score0.841

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.331
Teacher spread0.321 · 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 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

Citations12
Published2014
Admission routes2
Has abstractyes

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