Culturally Tailored Strategies In Pregnancy & Physical Activity Studies For South Asian Women: A Scoping Review
Bibliographic record
Abstract
INTRODUCTION: Majority of prenatal physical activity (PA) research has included homogenous populations and is significantly missing inclusion of racialized women. In particular, South Asian women have markedly low adherence to PA throughout pregnancy and this contributes to an increased risk for perinatal complications. Incorporating culturally tailored strategies into PA programs may be an effective way to increase uptake and adherence. In addition, culturally tailored strategies can address population-specific barriers and capitalize on motivators for PA. PURPOSE: This scoping review summarized culturally tailored strategies used in PA research with pregnant South Asian women. Secondly, population-specific barriers and motivators PA were identified to inform the development of future interventions. METHODS: The Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews checklist was followed. The key terms: “South Asian,” “pregnant,” and “physical activity” were searched in Medline, Web of Science, and EMBASE. Eligible studies included pregnant women who were South Asian and studies including an assessment of PA. Data were extracted, and a deductive approach was applied to categorize and summarize findings as culturally tailored strategies used and barriers and motivators to prenatal physical activity. RESULTS: N = 47 studies from South Asia, Europe, and Oceania were included. Seventeen studies reported using culturally tailored approaches, with the translation of study material into the population’s dominant language as the most frequent strategy. Cultural norms that did not favour active pregnancies, pregnancy physical symptoms (e.g., swollen feet, muscle pain), and the lack of knowledge surrounding safe prenatal physical activities were barriers South Asian pregnant women faced to being active. Motivators for being active included improved physical (e.g., improved blood pressure) and mental health (e.g., depression) and social support from family, friends, and health professionals. CONCLUSIONS: Future research and health promotion initiatives should incorporate culturally tailored approaches addressing population-specific barriers and leveraging motivators to promote PA among South Asian pregnant women.
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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 | 0.018 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".