Frequency, Severity, and Effect on Life of Physical Symptoms Experienced During Pregnancy
Bibliographic record
Abstract
This study aimed to: 1) describe the number, frequency, severity of discomfort and effect of symptoms on life of 29 physical symptoms women experienced at 15 to 25 weeks of gestation; 2) explore whether experiencing this group of physical symptoms more frequently and intensely was associated with a higher score of depressive symptoms and lower self-esteem; (3) examine whether discomfort and effect ratings aided prediction of well being over and above symptom frequency; and (4) investigate which individual physical symptoms contributed most to predicting depressive symptoms and self-esteem. Pregnant women (n = 215) completed the Beck Depression Inventory, Rosenberg Self-Esteem Scale, and a physical symptoms questionnaire. Frequency, discomfort, and the effect of physical symptoms all consistently correlated with higher scores for depressive symptoms, but less consistently with lower self-esteem. Discomfort and the effect of symptoms predicted variance in depressive symptoms after accounting for symptom frequency. Higher frequency, more discomfort, and the effect of fatigue and effect of flatulence were related to depressive symptoms. Relationships between pregnancy-related physical symptoms, depressive symptoms, and low self-esteem suggest that when women report any of these constellation of factors, further screening is indicated. A comprehensive assessment of physical symptoms includes frequency, discomfort, and effect on life.
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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 | 0.005 |
| 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".