The Effect of Maternal Anxiety/Depression on Breastfeeding Outcomes: MAVAN (Maternal Adversity Vulnerability and Neurodevelopment) Study
Bibliographic record
Abstract
Objectives We tested the hypothesis that mothers who experience substantial anxiety or depression at different time periods are at risk for reduced initiation, exclusivity and duration of breastfeeding. Methods Longitudinal data on mental health and infant feeding were collected on 257 Canadian women from 18–23 weeks gestation through 12 months postpartum. Multivariate logistic regression was used to assess whether scores on the Edinburgh Post‐Natal Depression Scale (EPDS), Hamilton Anxiety Scale (HAMA) and State‐Trait Anxiety Inventory (STAI) were associated with breastfeeding practices. Results In adjusted models, a single point increase in HAMA scores measured at 3 months postpartum was associated with an 11% reduction in the odds of exclusively breastfeeding at 6 months [aOR= 0.89, 95% (CI 0.80, 0.99)]; also a single point increase in STAI State and STAI Trait scores measured at 3 months postpartum was associated with a 4% reduction in odds [aOR= 0.96, 95% CI (0.92, 0.99)] and an 8% reduction in the odds of any breastfeeding at 12 months [aOR= 0.929, 95% CI (0.86, 1.00) ] respectively. Conclusion Our findings support a relationship between maternal anxiety and reduced breastfeeding but do not show significant associations between maternal depression and breastfeeding practices. Small sample sizes may have limited detection of significant findings. Grant Funding Source : None
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".