The influence of age regarding dietary choices of Indian immigrants in the United Kingdom
Bibliographic record
Abstract
Migrants are relatively healthy upon arrival in the host country, but over time their health deteriorates, and many of them acquire non-communicable diseases, leading to higher mortality rates compared to the host population (1) .Dietary choices of immigrant populations have a great impact on their health status.Identifying the issues related to dietary choices and patterns bears a great significance for health promotion.This study aimed to explore the dietary choices and the influencing factors behind those choices in Indian immigrants in the UK.A purposive sampling method was used to select participants based on identified independent variables such as age, religion, socioeconomic factors, and gender.Semi-structured, one-to-one interviews were conducted, and detailed information about dietary choices and influencing factors were collected from eighteen Indian immigrants in the UK.The interviews were audio-recorded, transcribed verbatim, and thematically analysed, following Braun & Clark's six steps guide (2) .Several preliminary themes, such as age, time constraints, taste preferences, convenience, increased availability of ready to eat food, and influence of globalisation, were identified.Age emerged as a major theme associated with influencing dietary choices.Many participants reported cutting down on or skipping meals with progressing age in an effort to lose weight or overcome a health problem."I never had weight issues per se, but since going menopause I have cut down on the amount of food that I consume.Three have gone to two meals, and a small snack".Some participants mentioned reducing meat intake for health reasons."I used to eat a lot of red meat, but I am cutting down now.Obviously, age now, you have to look after yourself".This research suggests that Indian immigrants become more cautious about their dietary intake with advancing age and associated health issues, in agreement with a Canadian study that also exhibited improvement in South Asians immigrants' dietary practices with growing age and length of stay in the host country (3) .This indicates that immigrants get motivated to improve their dietary patterns with progressing age, as health risks are more tangible in this age group.Subsequently, immigrants are more likely to be receptive to healthy eating interventions at this age.Therefore, an individually tailored, cultural, and age-appropriate nutritional education program could benefit the healthy ageing of the Indian immigrant population living in the UK.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".