The Thai version of the Montreal Children's Hospital Feeding Scale (MCH-FS): psychometric properties.
Bibliographic record
Abstract
OBJECTIVE: To standardize and evaluate the psychometric properties of the Thai version of the Montreal Children's Hospital Feeding Scale (MCH-FS). MATERIAL AND METHOD: The MCH-FS was translated and the cultural effects of the Thai version (Thai.MCH-FS) were reviewed. Caregivers of 200 children between the age of12 and 48 months were interviewed and completed the Thai.MCH-FS. In addition to demographic information, each child had a physical exam and anthropometric measures were taken. Each child was classified with or without feeding problems by at least two of three pediatricians who were blind to the results of the feeding scale. RESULTS: Internal consistency for reliability was high (Cronbach's alpha at 0.835). The area under the ROC curve was 0.864. With a discrimination score of 40, both sensitivity (72%) and specificity (80.67%) were at acceptable levels. Factor analysis resulted in three factors accounting for 52.3%. Of the 200 children, 150 children were classified with nofeedingproblems and 50 with feeding problems. There were no significant differences in the characteristics of the two groups; however the Thai.MCH-FS scores were significantly different for the two groups. CONCLUSION: The Thai version of the MCH-FS has been shown to be a valid and reliable short scale for detecting feeding problems in a pediatric care setting.
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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 | 0.003 | 0.013 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".