Food Skills and Their Relationship with Food Security and Dietary Diversity Among Asylum Seekers Living in Norway
Bibliographic record
Abstract
OBJECTIVE: To investigate the impact of food skills on food security and dietary diversity among asylum seekers living in Norwegian reception centers. DESIGN: Cross-sectional study. SETTING: Eight asylum reception centers. PARTICIPANTS: A total of 205 asylum seekers (131 men and 74 women) recruited through convenience sampling. MAIN OUTCOME MEASURES: Food skills were measured using questions from the Canadian Rapid Response on Food Skills and divided into cooking skills and shopping skills. Food security was measured with the 10-item version of the Radimer/Cornell Scale. The dietary diversity score was based on a 24-h recall. ANALYSIS: Bivariate and multivariable logistic regression. RESULTS: Cooking skills were associated with adequate dietary diversity (adjusted odds ratio, 2.12; 95% confidence interval, 1.04-4.31), but not with adult food insecurity (adjusted odds ratio. 0.63; 95% confidence interval, 0.26-1.53). Shopping skills were not associated with either measure of dietary diversity or adult food insecurity. Women had higher cooking skills than men, but there were no gender differences in shopping skills. CONCLUSIONS AND IMPLICATIONS: Food skills had a limited association with food security and dietary diversity. Further research is needed to identify food skills beneficial for asylum seekers and to address the multiple causes of food insecurity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".