The relationship of maternal anxiety, positive and negative affect schedule, and fatigue with neonatal psychological health upon childbirth
Bibliographic record
Abstract
BACKGROUND: Exposure of mothers to negative moods and stress before childbirth leads to negative consequences for the infants. Given the importance of psychological health, this study aimed to examine the effect of these factors on the infants' psychological health. METHOD: This cross-sectional study was conducted in Shiraz hospitals on 110 pregnant women selected with multistage random sampling. Research tools included The McGill Pain Questionnaire (MPQ) to measure fatigue with three criteria; The Positive and Negative Emotion Schedule (PANAS); and The Spielberger State-Trait Anxiety Inventory (STAI) were used to measure maternal mood and anxiety level. Also, neonatal psychological health was assessed by a checklist. Neonatal psychological health's correlation with maternal anxiety, fatigue, and mental state was assessed. Data were analyzed by SPSS-19 software using Pearson correlation coefficient and statistical regression at the significance level of 0.05. RESULT: Although there was no significant relationship between maternal anxiety score and neonatal psychological health after birth (p = 0.231; r=-0.343), the relationship was significant immediately after birth with positive (P < 0.001; r = 0.343) and negative affect scores (P < 0.001; r=-0.357). CONCLUSIONS: There was a statistically significant relationship between the neonatal psychological health and maternal fatigue (p ≤ 0.001; r = -0.357) and PANAS (p ≤ 0.001) of the mother; however, it had no significant relationship with maternal anxiety (p = 0.231; r=- 0.343). Therefore, nurses and midwives can reduce maternal anxiety and improve neonatal mental health by supporting mothers.
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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 | 0.000 | 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".