Development of the Get Active Questionnaire for Pregnancy: breaking down barriers to prenatal exercise
Bibliographic record
Abstract
Evidence-based guidelines represent the highest level of scientific evidence to identify best practices for clinical/public health. However, the availability of guidelines do not guarantee their use, targeted knowledge translation strategies and tools are necessary to help promote uptake. Following publication of the 2019 Canadian Guideline for Physical Activity throughout Pregnancy, the Get Active Questionnaire for Pregnancy, and an associated Health Care Provider Consultation Form for Prenatal Physical Activity were developed to promote guideline adoption and use amongst pregnant individuals and health care providers. This paper describes the process of developing these tools. First, a survey was administered to qualified exercise professionals to identify the barriers and facilitators in using existing prenatal exercise screening tools. A Working Group of researchers and stakeholders then convened to develop an evidence-informed exercise pre-participation screening tool for pregnant individuals, building from previous tool and survey findings. Finally, end-user feedback was solicited through a survey and key informant interviews to ensure tools are feasible and acceptable to use in practice. The uptake and use of these documents by pregnant individuals, exercise, and health care professionals will be assessed in future studies. Novelty: Evidence supports the safety/benefits of exercise for most pregnant individuals; however, exercise is not recommended for a small number of individuals with specific medical conditions. The Get Active Questionnaire for Pregnancy and Health Care Provider Consultation Form for Physical Activity during Pregnancy identify individuals where prenatal exercise may pose a risk, while reducing barriers to physical activity participation for the majority of pregnant individuals.
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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 | 0.038 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".