A measurement scale to assess responsive feeding among Cambodian young children
Bibliographic record
Abstract
The caregiver-child interaction during mealtime, which refers to responsive feeding (RF), influences child's dietary intake. In Cambodia, given the level of malnutrition, getting better knowledge of RF among young children is essential, but to do so, using an appropriate assessment tool is necessary. We aim to develop and to validate a measurement tool to assess RF in two different situations (before and after an intervention) among children 6-23 months old. This research is part of a larger trial assessing the impact of nutrition education combined or not with the provision of complementary foods on child nutritional status. The "Opportunistic Observation Form" from the Process for the Promotion of Child Feeding package was used to collect data on RF through direct observations of child's meal episodes. Data were used to define an initial scale composed of four constructs and 15 indicators. Confirmatory factor analyses (CFA) and Hancock and Mueller's H reliability indices were computed to assess the validity and reliability of the scale. The final tool was applied to baseline and endline data. At baseline, the sample included 243 pairs and, at endline, 248 pairs. The final scale included two latent constructs (RF and active feeding) that comprise three indicators for active feeding and five for RF. Criteria for fit indices of CFA were met for both constructs though better at baseline. Reliability coefficients were above 0.80 for each construct at baseline and endline. This research proposes a scale that could be used to assess active feeding and RF. Further validation is warranted in different contexts.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".