“I feel like a turkey. Not done super well but look okay to eat”: Exploring the complexities of motherhood and physical activity using autophotography
Bibliographic record
Abstract
Despite physical activity’s (PA) potential to positively support the mental well-being of postpartum women, recovery, sleep deprivation, parenting, and body image expectations can greatly impact postpartum PA engagement. Yet, self-efficacy and programming specifically for new mothers may facilitate PA postpartum. Guided by critical feminist methodologies, we used autophotography to explore the complexities of postpartum women’s PA experiences. Participants in a larger experimental PA-based study, designed for women within 6-month postpartum were invited to participate. Nine women shared up to six photos, each accompanied by a 2-3 sentence description, depicting their experiences of PA, motherhood, and self-efficacy, resulting in 48 photos. We used Braun and Clarke's (2019) reflexive thematic analysis to analyze data and represented each theme with rich and thick descriptions and a collage of images inspired by Bancroft's (2018) braided narrative. We uncovered four themes that reflected the complex and gendered nature of postpartum PA engagement. First, gendered expectations of motherhood placed demands on time and space for PA engagement. Second, how mothers felt about their bodies both positively and negatively impacted their sense of self and PA engagement. Third, moments of self-compassion illustrated how navigating feelings of self-compassion in relation to PA was messy and strained their well-being. Fourth, self-efficacy was essential, highlighting how a presence or lack of confidence surrounding PA capabilities impacted PA engagement. These findings expand the literature on postpartum women’s PA experiences and may inform inclusive PA programming for postpartum 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| 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".