Translation and cultural adaptation of the Health Utilities Preschool to Brazilian Portuguese
Bibliographic record
Abstract
INTRODUCTION: Health research is particularly important in low- and middle-income countries (LMICs), where improvements must be achieved with limited resources, and where the great majority of the world's population, especially children, live. Improvements in public health detection in Brazil have resulted in cancer becoming the most prevalent cause of death by disease in the group aged 1 to 19 years, hence, delivering cost-effective care to the group is a priority. Preference-based measures of health status and health-related quality of life (HRQL) integrate morbidity and mortality and provide utility scores for the estimation of quality-adjusted life years to be used in cost-effectiveness analyses and economic evaluation. The generic preference-based instrument Health Utilities - Preschool (HuPS) measures the health status of young children and is applicable to the age group 2 to 5 years, who carry the highest incidence of cancer in childhood. METHODS: The translation of the HuPS classification system followed recommended protocols from published guidelines. Forward and backward translations were performed by a team of six qualified professionals and linguistic validation was undertaken with a sample of parents of preschool children. MAIN RESULTS: Initial disagreements on individual words occurring in 0.5-1.5% were resolved by consensus. A final version of the instrument was validated by the sample of parents. CONCLUSIONS: The translation and cultural adaptation of the HuPS into Brazilian Portuguese were accomplished as the first step in the validation of the HuPS instrument in Brazil.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".