Breastfeeding Performance Among Potentially Depressed Nursing Mothers
Bibliographic record
Abstract
BACKGROUND: The maternal process is vulnerable for women to fall in an anxiety state that refers to postpartum depression. When symptoms appear, the possibility of depression during pregnancy will have a direct impact on the initiation of early breastfeeding and the termination of early breastfeeding. PURPOSES: This study aims to look at the relationship between the potential of postpartum depression and the performance of breastfeeding in nursing mothers. This study used a cross-sectional study approach, in one of the sub-districts in Makassar City with the lowest achievement of exclusive breastfeeding. METHODS: The study subjects were postpartum mothers who fulfilled 225 eligibility sampling throughout the period March-August 2018. Sociodemographic, social support, obstetric variables, potential maternal postpartum depression, and breastfeeding performance assessment were collected and analyzed using the chi-square test and independent-sample t-test. RESULTS: The study show that age (<0.001), work profile (<0.001), living property (<0.006), number of children (<0.001), and family support (<0.001) have been shown to influence maternal depression. CONCLUSION: This study conclude that sociodemographic factors, especially economic vulnerability and social support, are risk factors for depression in nursing mothers. Although it did not appear to be different from breastfeeding performance between mothers who experienced depressive symptoms and anxiety, both felt the same of the obstacles to breastfeeding techniques. Therefore, this study recommend for all mother and child services to performed screening for depression symptom in term of pregnancy, and provide them skill for better lactation.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".