What impact does pregnancy have on anxiety about health?
Bibliographic record
Abstract
BACKGROUND: A previous study suggests that health anxiety, or preoccupation and fears about ill health, is elevated during pregnancy. However, replication of this result is needed given several methodological weaknesses of the previous research. The current study refined earlier work by assessing health anxiety using two distinct measures and comparing scores to a control group and to established norms for healthy controls. The relationship of health anxiety to background variables such as parity and pregnancy complications was also explored. METHODS: A total of 252 women in the third trimester of pregnancy and 45 similarly aged non-pregnant women completed the Illness Attitudes Scale (IAS) and the newly developed Short Health Anxiety Inventory (SHAI). RESULTS: Compared to the non-pregnant sample and established scores for healthy controls, health anxiety was not elevated during pregnancy. Health anxiety was higher in women who experienced complications during pregnancy but was unrelated to other background variables. The IAS identified more individuals as health anxious than the SHAI. CONCLUSIONS: Contrary to previous research, health anxiety was not elevated during pregnancy. The IAS appeared to be susceptible to identifying women as health anxious due to greater health care utilization by pregnant women rather than higher health anxiety. Clinical recommendations and future directions for the assessment of health anxiety are outlined.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".