CROSS-CULTURAL ADAPTATION AND VALIDATION OF THE MONTREAL CHILDREN’S HOSPITAL FEEDING SCALE INTO BRAZILIAN PORTUGUESE
Bibliographic record
Abstract
OBJECTIVE: To cross-culturally adapt and validate the Montreal Children's Hospital Feeding Scale (MCH-FS) into Brazilian Portuguese. METHODS: The MCH-FS, originally validated in Canada, was validated in Brazil as Escala Brasileira de Alimentação Infantil (EBAI) and developed according to the following steps: translation, production of the Brazilian Portuguese version, testing of the original and the Brazilian Portuguese versions, back-translation, analysis by experts and by the developer of the original questionnaire, and application of the final version. The EBAI was applied to 242 parents/caregivers responsible for feeding children from 6 months to 6 years and 11 months of age between February and May 2018, with 174 subjects in the control group and 68 ones in the case group. The psychometric properties evaluated were validity and reliability. RESULTS: In the case group, 79% of children were reported to have feeding difficulties, against 13% in the control group. The EBAI had good internal consistency (Cronbach's alpha=0.79). Using the suggested cutoff point of 45, the raw score discriminated between cases and controls with a sensitivity of 79.4% and specificity of 86.8% (area under the ROC curve=0.87). CONCLUSIONS: The results obtained in the validation process of the EBAI demonstrate that the questionnaire has adequate psychometric properties and, thus, can be used to identify feeding difficulties in Brazilian children from 6 months to 6 years and 11 months of age.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".