Adapting an Evidence-Based Infant Feeding and Nutrition Program to Promote Healthy Growth and Development in Latinx Families of Low Income
Bibliographic record
Abstract
OBJECTIVE: To describe the cultural adaptation of an evidence-based early childhood obesity prevention intervention for Latina mothers and nonmaternal caregivers in families of low income who speak diverse languages. DESIGN: Participatory intervention adaptation methods were used to complete the first and second steps of the cultural adaptation process: (1) gathering information and (2) preliminary intervention adaptations. SETTING: Southern California, US. PARTICIPANTS: Mothers of infants aged 4-6 months and their trusted caregivers (n = 46 mother-caregiver dyads) and members of an intervention workgroup (n = 8). INTERVENTION: Healthy Beginnings Communicating Healthy Beginnings Advice by Telephone was adapted for Latina mothers and nonmaternal caregivers. MAIN OUTCOME MEASURES: Mother and caregiver recommendations for intervention design. ANALYSIS: We used qualitative approaches to analyze textual data for the cultural adaptation process. Notes and observations from the recorded intervention workgroup meetings were incorporated into the intervention design. RESULTS: Content adaptations included cultural meanings of infant feeding, maternal mental health, and infant feeding practices. Intervention delivery changes included caregiver involvement, reduced in-home session time, increased session frequency, and intervention delivery by the community health workers. CONCLUSIONS AND IMPLICATIONS: Nonmaternal caregivers play an important role in intervention adaptation by ensuring that early childhood obesity prevention efforts are culturally and linguistically relevant.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".