Physical Activity during Pregnancy may Mitigate Adverse Outcomes Resulting from COVID-19 and Distancing Regulations: Perspectives of Prenatal Healthcare Providers in the Southern Region of the United States
Bibliographic record
Abstract
International Journal of Exercise Science 14(3): 1138-1150, 2021. Prenatal physical activity (PA) may mitigate adverse outcomes that have increased as a result of the coronavirus pandemic, including poor maternal mental health. This study explored the perspectives of prenatal healthcare providers (PHCP) on maternal PA during the pandemic and identified resources providers would like to have to inform clinical discussions and prescription of PA. Semi-structured interviews were completed with PHCPs following a qualitative description approach. A content analysis coded data to inform three study objectives: 1. Changes to maternal health, 2. The role prenatal PA can have during a pandemic, 3. Resources PHCPs would find helpful to discuss and prescribe PA. Nine PHCPs completed interviews. Changes to maternal health include an increase in stress, fear surrounding labor and delivery, and risk of pre-existing problematic behaviors (e.g., substance abuse). PA was identified as helpful for improving mental health and preventing excessive gestational weight gain (EGWG). Providers expressed interest in having low cost referral options for prenatal PA that are accessible from home. PHCPs suggest PA during the pandemic can improve maternal mental health and prevent EGWG. To support clinical discussions and prescriptions of prenatal PA, knowledge translation initiatives should include informing PHCPs of referral resources for low cost at-home fitness options.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".