The nutrition transition in the Republic of Ireland: trends in energy and nutrient supply from 1961 to 2007 using Food and Agriculture Organization food balance sheets
Bibliographic record
Abstract
Over the course of the last 50 years the Republic of Ireland has gone from being one of the poorest countries in Europe to one of the richest; however, it is now experiencing increasing rates of obesity and non-communicable chronic disease. Although several national nutrition surveys have been carried out in Ireland since 1990, there is little information on the Irish diet before then. We analysed the FAO food balance sheets for Ireland from 1961 to 2007 in order to characterise the changes in energy and nutrient supply that took place during that period. Food balance sheets were downloaded from the FAOSTAT database and per capita supply of commodities was analysed using dietary analysis software. Energy from carbohydrate as a percentage of total energy fell from 55 % in 1961 to 46 % in 2007, whereas energy from fat increased from 29 % to 34 %; these values are well outside WHO recommendations for the prevention of chronic disease. Energy from alcohol as a percentage of total energy has doubled within the last 20 years. On a nutrient-density basis, vitamins and minerals met or exceeded WHO recommendations, apart from vitamin D, folate, Ca and Fe. Although there are methodological limitations associated with the use of food balance sheets, the present results demonstrate that the current imbalances in the Irish diet were already evident several decades ago. Because they are so long established, they will be difficult to reverse unless major public health nutrition interventions are implemented.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".