Translation, adaptation, and cross-cultural validation into Brazilian portuguese of the hearing protection assessment questionnaire (HPA)
Bibliographic record
Abstract
ABSTRACT Purpose The aim of the present study is to translate, adapt, and cross-culturally validate the Brazilian Portuguese version of the questionnaire Hearing Protection Assessment Questionnaire (HPA). Methods The original instrument, developed in English, seeks to assess barriers and supports related to the use of hearing protection devices (HPD), as well as workers' knowledge, habits and attitudes towards occupational noise. The translation, adaptation, and cross-cultural validation of the questionnaire consisted of five steps: Translation of the questionnaire from English to Portuguese; 2) Reverse translation from Portuguese to English; 3) Analysis of the instrument by three experts in the field; 4) Pre-test of the questionnaire with ten workers; 5) Application of the instrument to 509 workers in a meatpacking industry after the pre-employment medical exam. Results The results indicate the construction and content validity of the Brazilian Portuguese version for use with a working population and its internal consistency. Conclusion This study resulted in the translation, cultural adaptation, and validation of the Hearing Protection Assessment Questionnaire (HPA), in order to be used to assess the use of individual hearing protection in the occupational field, called Hearing Protection Assessment Questionnaire (HPA).
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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 | 0.018 | 0.033 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".