Translation of the Pediatric Nausea Assessment Tool (PeNAT) Into Spanish and Evaluating Understandability Among Spanish-Speaking Hispanic American Children and Adolescents Receiving Chemotherapy
Bibliographic record
Abstract
INTRODUCTION: We aimed to create a Spanish-language version of the Pediatric Nausea Assessment Tool (PeNAT) and examine its understandability among Spanish-speaking, Hispanic American children. METHODS: : Forward and backward translations of the PeNAT documents were performed and verified by a bilingual panel. Four monolingual, Spanish-speaking dyads (child/parent) and four bilingual dyads piloted the Spanish-language PeNAT documents. Four additional bilingual dyads read both versions and completed the PeNAT using their preferred version. These were reviewed for errors due to misunderstanding. UNDERSTANDABILITY: Children aged 4-18 years about to receive chemotherapy who spoke Spanish at home and were without impairments precluding PeNAT use were eligible. Participants used the Spanish-language PeNAT during a chemotherapy block. Parents gave feedback on the PeNAT documents. Recruitment continued until 10 consecutive participants offered no substantive suggestions for revision. RESULTS: : Ten cancer patients (mean age: 10.6 years) used the Spanish-language PeNAT. All parents felt their child understood the PeNAT; none felt the documents were hard or very hard to use. CONCLUSION: The Spanish-language PeNAT was understood by Spanish-speaking Hispanic American children. Further psychometric testing is warranted.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".