Developing a new measure of retrospective body dissatisfaction: links to postnatal bonding and psychological well-being
Bibliographic record
Abstract
BACKGROUND: Pregnancy is a transformative time for women and their bodies, and therefore thoughts and feelings about the body understandably change during this period. While previous research has established the impact of body dissatisfaction on factors like antenatal attachment and maternal mental health, there is a notable gap in understanding its long-term effects on postnatal factors. This is often due to high attrition rates in longitudinal studies. Using retrospective measures could address this issue, however a measure of retrospective pregnant body dissatisfaction has not yet been identified. AIMS: This paper aimed to create a retrospective measure of pregnancy body dissatisfaction by adapting a previously validated measure. It also aimed to investigate the relationship between retrospective accounts of body dissatisfaction during pregnancy and postnatal anxiety, depression, and bonding. METHOD: = 404). FINDINGS: An exploratory and confirmatory factor analysis identified a two-factor model of retrospective body dissatisfaction, adapted from the Body Understanding Measure for Pregnancy Scale, which was equivalent to two of the original subscales. Using this factor structure, linear regressions demonstrated that higher levels of retrospective pregnant body dissatisfaction were associated with elevated rates of postnatal anxiety and depression and lower bonding scores. CONCLUSIONS: This study successfully established a measure for assessing retrospective pregnant body dissatisfaction, potentially aiding future research. Additionally, it has highlighted the link between pregnant body dissatisfaction and postnatal levels of depression, anxiety, and bonding. Thus, improving the pregnant bodily experience may have the potential to enhance the postnatal experience.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".