Translation and cross-cultural adaptation of the Get Active Questionnaire for Pregnancy (“Questionário Seja Ativa durante a Gravidez”) into Portugal and Brazil Portuguese
Bibliographic record
Abstract
Physical activity (PA) is established to be an essential component of a healthy lifestyle across the lifespan and is highly recommended during pregnancy. Guideline recommendations used to call for all women to speak with their health care provider before beginning or continuing PA during pregnancy. However, these have been removed from more recent guidelines due to the strong evidence supporting the safety and benefits of prenatal PA. In 2021, the Canadian Society for Exercise Physiology (CSEP) developed and released an instrument called Get Active Questionnaire for Pregnancy (GAQ-P) to identify the small number of women who should seek medical advice as a first step to becoming or continuing to be physically active during the months that they are pregnant, and to help the majority of healthy pregnant women overcome any concerns they might have with getting or staying active. The original instrument was developed in English. This article describes the translation and cross-cultural adaptation of the GAQ-P for use in the Portuguese language (both Brazil and Portugal). We followed the ten-step translation process outlined by the Translation and Cultural Adaptation International Society for Pharmacoeconomics and Outcomes Research guidelines. Our template can be used by other health professionals for translation and verification of the original tool into their native language.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.008 | 0.003 |
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".