A Delphi Study to Identify Research Priorities Regarding Physical Activity, Sedentary Behavior and Sleep in Pregnancy
Bibliographic record
Abstract
This study aimed to produce a list of the top 10 research priorities regarding physical activity, sedentary behavior, and sleep in pregnancy. Using the Delphi methodology, pregnant/postpartum individuals (N = 118), exercise professionals and healthcare providers (N = 55) listed up to 10 questions perceived as unanswered regarding physical activity, sedentary behavior, and sleep in pregnancy (Round 1). Respondents rated the proposed questions on a Likert importance scale (Round 2), and the sum of ratings received were totaled. Questions of priority regarding physical activity among pregnant/postpartum individuals (N = 67), healthcare providers and exercise professionals (N = 22) pertained to exercise prescription, impact of exercise on maternal and fetal outcomes and impact of exercise on pregnancy conditions, special population groups and clinical education and access to information. Sedentary behavior priorities included the impact of sedentary behavior on maternal and fetal outcomes, sedentary recommendations and exercise and sedentary positioning. Sleep research priorities included the impact of pregnancy on sleep, safety, sleeping aids and the effect of exercise on sleep. Pregnant/postpartum women, healthcare providers and exercise professionals prioritized questions that have in part been addressed by existing research, highlighting a need for improved knowledge translation from research to practice. They have also identified novel questions that warrant prioritization within future research.
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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 | 0.089 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".