A comparative study of psychological features before first antenatal ultrasonography in high-risk versus normal pregnancy
Bibliographic record
Abstract
Abstract: BACKGROUND: High-risk pregnancies involve medical or obstetric conditions that pose hazards to the health of the mother or fetus, potentially impacting psychological well-being. Routine ultrasound (USG) scans are essential for monitoring pregnancy health, but may also influence maternal anxiety, stress, and depression, particularly in high-risk cases. AIM: The aim of the study was to investigate the psychological features in women with high-risk and normal pregnancies before their first antenatal ultrasound. MATERIALS AND METHODS: A cross-sectional study was conducted at a tertiary care teaching hospital involving 250 participants (125 high-risk and 125 normal pregnancies) after Institutional ethical clearance. Participants were assessed using a semi-structured pro forma for socioeconomic and clinical data, along with the World Health Organization Depression Anxiety Stress Scale-42 scale to measure depression, anxiety, and stress levels. Data were analyzed using t -tests for continuous variables and Chi-square tests for categorical variables, with significance set at P < 0.05. RESULTS: No significant differences were observed in mean age, occupation, place of residence, or socioeconomic status between high-risk and normal pregnancy groups. However, consanguinity was significantly higher in high-risk pregnancies (16% vs. 2.4%). High-risk pregnancies showed a longer duration to conceive the first child and older age at marriage. In addition, medical comorbidities such as anemia, diabetes, and hypertension, and a history of infertility were more prevalent in high-risk pregnancies. Family pressure to conceive was significantly higher in the high-risk group. Depression, anxiety, and stress scores were notably elevated in high-risk pregnancies compared to normal pregnancies. CONCLUSION: Women with high-risk pregnancies experience significantly higher psychological distress compared to those with normal pregnancies. These findings underscore the need for integrated care that includes psychological support for women facing high-risk pregnancies to enhance maternal well-being and pregnancy 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 | 0.003 |
| 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 | 0.000 | 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".