I'll Never Give Up: A Qualitative Study of Caregivers’ Perceptions and Decisional Processes When Feeding Infants and Toddlers Novel and Disliked Foods
Bibliographic record
Abstract
OBJECTIVE: To better understand caregivers' decisional processes related to offering novel and disliked foods to their infants and toddlers. DESIGN: As part of a parent study on young children's food acceptance that took place in Denver, CO, this secondary analysis used a basic qualitative approach to explore caregivers' decisional processes related to repeated exposure and children's food rejection. PARTICIPANTS: English-speaking caregivers of infants and toddlers (aged 6-24 months; n = 106) were recruited via flyers and social media and interviewed (from July, 2017 to January, 2018) during a laboratory visit focused on introducing a novel food. PHENOMENON OF INTEREST: Factors influencing caregiver decisions to (dis)continue offering novel or disliked foods. ANALYSIS: Using a combined deductive and inductive coding approach, trained researchers coded transcripts and codes, which were reviewed and discussed by all investigators to identify themes. RESULTS: Three major themes (and 2 subthemes) were generated regarding caregivers' decisions about re-offering rejected foods: 1) Caregivers understand that multiple experiences with new foods are needed because children's reactions can be unpredictable and depend upon time, developmental stage, and child traits; 2) Caregivers vary in their persistence and decisions to keep offering foods depending on responsiveness to child cues (sub-theme) and adult-centered beliefs, needs, and decisions (sub-theme); 3) Child food acceptance will change with time, circumstances, and development if you keep trying. CONCLUSIONS AND IMPLICATIONS: Although caregivers are aware of repeated exposure, additional implementation research focused on translating theory into effective home practices could assist caregivers to persist in offering novel or disliked foods.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".