Spending Longer Time in the Kitchen Was Associated With Healthier Diet Among Japanese Older Women With Frailty
Bibliographic record
Abstract
OBJECTIVE: To evaluate the conditional effect of time spent in the kitchen on the association between frailty status and healthy diet among older women. DESIGN: Secondary analysis of an online cross-sectional survey conducted in January 2023. PARTICIPANTS: Six hundred Japanese women (aged ≥ 65 years). MAIN OUTCOME MEASURE(S): Frailty status evaluated using the Kihon Checklist (25 affirmative questions assessing daily functions, weight status, and mental condition); healthy diet assessed by the days of consuming ≥ 2 meals that include staple, main and side dishes in a meal (SMS meal) in a day; and time spent in the kitchen. ANALYSIS: Moderation analysis was used to evaluate the conditional effect of time spent in the kitchen on frailty status and SMS meal intake. Chi-square tests for independence were used to evaluate the differences in the Kihon Checklist items by frailty status. RESULTS: Spending longer time in the kitchen indicated more frequent SMS meal intake and the trend was stronger among older women with frailty than those with robustness. All items except for 1 item regarding weight status (P = 0.15) were significantly associated with frailty status (P < 0.001). CONCLUSIONS AND IMPLICATIONS: Further studies are needed to evaluate the causal relationship between frailty status, healthy diet, and kitchen use.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".