Factors Associated With Postpartum Weight Retention in African Women: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: The obesity epidemic among women in Africa is a health problem, and many studies attribute it to childbearing. However, most studies of postpartum weight retention (PPWR) occur in high-income countries. OBJECTIVE: Therefore, this review sought to identify the potential factors affecting PPWR among African women. METHODS: Four databases were searched from January 2000 to December 2020: Medline/PubMed, Google scholar, Ajol research, FreeFullPDF. The quality of included studies was assessed using the Newcastle Ottawa Scale. RESULTS: Fifteen studies (5 from west, 4 from south, 3 from east, 2 from central, and 1 from north) were included: 8 cohort and 7 prospective cohort studies. Two studies examined the effect of obesity and weight gain during pregnancy on PPWR, 3 studies assessed the effect of childbirth, 4 examined the effect of breastfeeding, 4 assessed the impact of morbidities such as HIV, and 2 looked at food insecurity. Five studies demonstrated that postpartum weight is due to residual pregnancy weight gain and childbirth weight gain and is accentuated as parity increases (n = 2). Breastfeeding has a controversial effect, while morbidity (n = 4) and food insecurity (n = 4) contributed to weight loss. The variation in weight was also influenced by cultural practices (n = 1), prepregnancy weight (n = 1), and socioeconomic status (n = 1). On all domains, only 3 included studies were of good quality. CONCLUSIONS: Pregnancy weight gain, childbirth, breastfeeding, morbidity, and food insecurity were associated with PPWR. However, preexisting factors must be considered when developing PPWR modification strategies. In addition, due to the limited number of studies included, robust conclusions cannot be drawn.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".