Food Intake and Food Selection Following Physical Relocation: A Scoping Review
Bibliographic record
Abstract
Objectives: To synthesize the current available evidence on the changes in food intake and food selection after physical relocation in non-refugee populations. Methods: The inclusion criteria were studies with a measurement of food selection and/or food intake in non-refugee populations where physical relocation had occurred with self-reported or objective assessment of the neighbourhood physical environment before and after relocation. Databases searched included MEDLINE, EMBASE, CINAHL and SCOPUS from 1946 to August 2022. Results: A total of four articles met the inclusion criteria. Overall, these studies gave longitudinal (n = 2) and cross-sectional (n = 2) evidence to suggest that moving to an urban neighbourhood with more convenience stores, cafés and restaurants around the home was associated with an increase in unhealthy food intake in adult populations. Additional factors such as income, vehicle access, cost, availability and perceptions of the local food environment played a role in shaping food selection and food intake. Conclusion: Four internal migration studies were found. The limited evidence base calls for more research. Future studies should include children and apply appropriate research designs to account for neighbourhood self-selection and concurrent life events. International migration studies should include assessment of neighbourhood physical environments pre- and post-relocation.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".