Food Intake Behavior and Chronotype of Japanese Nurses Working Irregular Shifts
Bibliographic record
Abstract
Shift work is the popular working pattern in many fields in industrialized nations. However, the shift workerdoes not pay much attention to his (her) own health. It is known that shift work has strong associations withvarious diseases. The aim of this study was to investigate the relationship between food intake and chronotype inJapanese nurses working an irregular rotation of shifts. This questionnaire-based study used a cross-sectionaldesign. Participants were nurses working in several hospitals, data from 159 respondents being analyzed. Thequestionnaire covered demographics, the Diurnal Type Scale (DTS) and a Food Intake Questionnaire (FIQ). TheDTS scores were classified into three chronotype groups: modified Morning-type (M-type), modifiedEvening-type (E-type) and modified Intermediate-type (I-type). For food intake behavior, meal habits of theM-types were compared with the E-types before / after day- and night-work. In the morning, just after thenight-shift, the M-types chose cold food more frequently (p = .016) and felt less satiated after the meal (p = .016)than the E-types. Furthermore, the E-types chose significantly larger meals (p = .023) than the M-types, theM-types snacking more frequently. Chronotype was associated with the food intake behavior both in day- andnight-shift. These results suggest that the Morning-type person suffered more inconvenience with regard to foodintake behavior during night-work.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".