Diet, health and the nutrition transition: some impacts of economic and socio-economic factors on food consumption patterns in the Kingdom of Tonga.
Bibliographic record
Abstract
An essential element of the "health transition" is the emergence of disease patterns associated with changes in dietary regimes. The consumption of nutritionally poor (imported) foods in the Pacific is associated with increasing rates of diet related non-communicable diseases (NCDs). An oft-made assumption is that changes in consumption patterns are related to food preference (specifically preferences for high fat and/or dense carbohydrate foods). Recent work in the Kingdom of Tonga suggests that the "common-sense" association between food preference and food consumption is incorrect. The results of a large survey (n=430) indicate availability is the key factor in consumption, and that food preference, knowledge of the nutritional values of foods, and frequency of consumption are not correlated. Further analysis shows there are significant differences in consumption patterns between persons of higher and lower socio-economic status; perception of availability and frequency of consumption are a function of economic and social position--specifically access to cash. These results underline the salience of economic factors; the rise in NCDs is correlated with the increasing importance of the cash economy (not cultural values or ignorance of nutritional issues). In the absence of economic solutions, current consumption patterns will continue.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".