Common Feeding Practices Used by Chinese Caregivers of 6–10-Month-Old Infants: A Behavioral Observation Study in Shaanxi Province
Bibliographic record
Abstract
OBJECTIVE: To explore the feeding practices and feeding environment of Chinese families with 6-10-month-old infants. DESIGN: One day of caregiver-recorded feeding occasions. SETTING: Homes in Shaanxi, China. PARTICIPANTS: Families recruited using convenience sampling. VARIABLES MEASURED: Videos were coded for feeding practice frequency and acceptance rate, feeding environment, and responsiveness to infant fullness cues and eating pace. ANALYSIS: Wilcoxon rank sum tests examined the differences in feeding practice use based on caregiver type, infant sex, and infant weight status. RESULTS: Twenty-eight families provided videos for coding. The most commonly observed feeding practices were opening the mouth, giving instructions, and interfering with the child's actions. Prompts to eat were accepted 86.9% of the time. Overweight infants' caregivers used significantly more coercive prompts to eat than did caregivers of healthy-weight infants (P < 0.05). Mothers used more autonomy-supportive prompts to eat than did fathers (P < 0.05). Early, active, and late infant fullness cues were captured in 25.6%, 34.8%, and 8.5% of videos, respectively. 53.6% of caregivers fed at the right pace, whereas 14.5% and 31.9% fed too slow or too fast, respectively. Approximately 5.5% of videos had a screen on, and 33.5% of videos included at least 1 other distraction during the meal. CONCLUSIONS AND IMPLICATIONS: Differences in feeding practices among caregivers suggest that targeted advice may further improve feeding practices. Improving the caregiver's ability to identify satiety cues and respond to infant eating speed may also reduce the risk of overfeeding.
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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 | 0.000 |
| 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".