Prenatal Subjective Social Status as a Predictor of Breastfeeding Outcomes
Bibliographic record
Abstract
Introduction: Subjective social status (SSS), which describes one’s perceived hierarchical rank, may provide a more accurate prediction of health than traditional socioeconomic measures. The specific relationship between SSS and breastfeeding outcomes is understudied. Our objectives were to: (1) investigate factors associated with SSS, (2) assess the relationship between prenatal SSS and any or exclusive breastfeeding at 3 and 6 months, and (3) evaluate the relationship between prenatal SSS and breastfeeding duration. Methods: Overall, 2,870 participants from the Canadian Healthy Infant Longitudinal Development birth cohort data were included. Prenatal SSS was self-ranked at 18 weeks' gestation using the MacArthur Scale and categorized as low (1–5), moderate (6–7), and high (8–10) SSS. Any and exclusive breastfeeding were categorized at 3 and 6 months postpartum, along with breastfeeding duration. Multivariable logistics and linear regression were conducted. Results: After adjusting for race, education, income, marital status, employment, and parity, the odds of exclusive breastfeeding at 3 months were 1.51 times higher [95% confidence interval (CI): 1.18, 1.94] in the high SSS group than in the low SSS group. The likelihood of any breastfeeding at 6 months was 1.45 times higher [95% CI: 1.10, 1.92] in the moderate SSS group than the low SSS group. Women with high SSS breastfed for approximately 42 days longer than women with low SSS. Conclusion: SSS was associated with exclusive breastfeeding at 3 months, any breastfeeding at 6 months, and breastfeeding duration. SSS may offer a more patient-centered indicator of health, informing targeted interventions for individuals at risk of suboptimal breastfeeding outcomes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".