'I just don't trust my pelvic floor': Examining the bio-social barriers to maternal health and physical activity participation in a sample of mothers' from New Zealand
Bibliographic record
Abstract
Maternal physical activity participation is associated with a range of positive mental and physical health outcomes. While a range of psycho-social and practical barriers to women's participation have been identified, little research has examined how these intersect with the physiological barriers posed by birthing injuries and the physical changes wrought by pregnancy and childbirth. In this study we address this gap using data collected through in-depth interviews with 20 women in Aotearoa New Zealand seeking to engage in regular exercise following childbirth. Guided by a socio-material conceptual framework, we examine women's physical activity engagement as bio-social phenomena. We pay particular attention to how women describe and navigate challenges posed by physiological considerations such as pelvic floor dysfunction, fatigue, the energetic demands of breastfeeding, and lingering birthing injuries. We argue these physical barriers are largely glossed over by policies, programmes and existing research that focus on motivating mothers to return to or become physically active. We suggest more sophisticated conceptual approaches are needed that explicitly acknowledge the overlapping biological and social dimensions of maternal physical activity in order to better support women through the complex postpartum period and beyond through enhanced resources, information, and general social awareness.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".