Correlation Between Mothers’ Depression and Developmental Delay in Infants Aged 6-18 Months
Bibliographic record
Abstract
BACKGROUND: Regarding the importance of children's developmental status and various factors that delay their development, this study was conducted to examine the correlation between mothers' depression levels and the developmental delay in infants. METHODS: This descriptive study was performed on 1053 mothers and their infants' age 6 to18 month-old in medical centers affiliated with Shahid Beheshti University of Medical Sciences, Iran, in 2014-2015. The participants were selected through multi-stage random sampling. The following instruments were used in this study: A demographic and obstetric specification questionnaire, infant specification questionnaire, the Beck Depression Inventory, and the Ages and Stages Questionnaire to determine the status of the children's development. The data were analyzed using SPSS19 software, Mann-Whitney; independent T-test and logistic-Regression tests were used. RESULTS: The results showed that 491 mothers (46.7%) suffered mild to extremely severe depression. The delay in infant development was 11.8%. The Mann-Whitney test showed a correlation between mothers' depression levels and developmental delay in infants (P=0.001). Moreover, there was a significant correlation between mothers' depression and developmental delays in gross-motor and problem-solving skills (P<0/05). In logistic model age of infants showed significant correlation with developmental delay (P=0.004 OR=1.07), but unwanted pregnancy, gender of infants, type of delivery and socioeconomic status had no correlation with developmental delay. CONCLUSION: Given the correlation between mothers' depression and infant development, it is recommended to screen mothers for depression in order to perform early interventions in developmental delay.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".