Dietary difficulties among vulnerable people affected by the Kumamoto Earthquake
Bibliographic record
Abstract
This study aimed to reveal dietary difficulties among vulnerable people affected by the Kumamoto Earthquake. We analyzed 148 copies of assessment sheets for special need diets recorded by the members of Japan Dietetic Association-Disaster Assistance Team dispatched to the damaged areas and summarized their complaints by life stage and symptoms. A total of 432 people were grouped into four life stages; three lactating woman, three infants, 13 1-to-6 year-old children, and 282 elderly people. The two most complaints by the elderly were “low in vegetables” (17.6%) and “meal is hard” (14.4%). It is often pointed out the shortage of vegetables in the shelter meals, but because elderly people usually eat more vegetables than in other ages, it is possible that they may feel a lack of vegetables strongly during a disaster. According to the analysis by symptoms, hypertension (41.0%) was the most prevalent followed by constipation (21.7%), diabetes (19.7%), and difficulty in eating and swallowing (11.1%). They were frequently observed symptoms among elderly people and also reported in the Great East Japan Earthquake. It was suggested that dietary difficulties among vulnerable people could be caused by lacks of information about special needs diets and food distribution system as well as diets low in vegetables.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".