Women moving forward in pictures: using digital photographs to explore postpartum women’s physical activity experiences
Bibliographic record
Abstract
While much research sees physical activity as an intervention for the postpartum body, there is limited literature understanding how postpartum physical activity affects women’s mental health and physical well-being. Unpacking how physical activity affects postpartum women holistically is critical because of the negative physical and mental health consequences accompanying the postpartum period. Thus, the purpose of this study was to use digital photographs to explore women’s experiences engaging in physical activity during the first-year postpartum. Auto-photography was used as it allowed postpartum women to share a photograph illustrating their physical activity experiences. This method allowed for comprehension regarding how participants believed physical activity impacted their mental health and physical well-being. Fifty women (Mage = 31.82 years; Mage of infant = 6.22 months) submitted a photo with a short text description explaining the photo context and what it represented. A reflexive thematic analysis was used to analyse the photos through a critical feminist lens. Study findings were organised into three themes. First, postpartum women engaging in physical activity experienced feelings of empowerment that helped heal the body and mind while reconnecting with their athletic identities. Second, doing so meant adapting their physical activity to motherhood or around motherhood. Third, postpartum women navigated many obstacles, including the COVID-19 pandemic, weather, and finding activewear that fit their changing bodies. Insights into these experiences may inform health promoters, healthcare professionals, recreation leaders, and women’s support networks to understand their needs when engaging in physical activity during the postpartum period.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".