Translation and cross-cultural adaptation of the Get Active Questionnaire for Pregnancy in Colombian Spanish
Bibliographic record
Abstract
To ensure safe, optimal, and personalized physical activity, exercise, or sport during pregnancy, the Canadian Society for Exercise Physiology, the Society of Obstetricians and Gynaecologists of Canada, the College of Family Physicians of Canada, and the Women's Health Division of the Canadian Physiotherapy Association, developed the Get Active Questionnaire for Pregnancy (GAQ_P) as a pre-assessment to identify women who may have a relative or absolute contraindication to prenatal exercise that requires further consultation with a health professional to determine if exercise can or should be continued or initiated during pregnancy. This study aims to translate and cross-culturally adapt the GAQ_P for use in Colombian Spanish. The original instrument was developed in English and French for the evaluation of the health of pregnant women before the beginning of physical activity and the guidelines for the same. Ten steps were followed according to the International Society for Pharmacoeconomics and Outcomes Research Translation and Cultural Adaptation guidelines, with the participation of four experts. The comprehensibility of the instrument was 99%, which shows a high percentage of intelligibility of the document. This article describes the translation and cross-cultural adaptation of the GAQ_P for use in Colombian Spanish, contributing positively to pre-exercise screening during pregnancy in Colombia.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".