DEVELOPMENT AND VALIDATION OF AN ANXIETY SCALE FOR PREGNANCY
Bibliographic record
Abstract
This research project developed and validated The Anxiety Scale For Pregnancy (ASP), which was based on Spielberger's research of state anxiety. An extensive review of the pregnancy literature led to the construction of an initial 82 items for this measure. They were pre-tested by a group of experts in the field of pregnancy and a sample of 40 women. The revised items comprised ten dimensions of pregnancy and 73 items relating to those dimensions and. was field tested on a group of 270 pregnant women. Validation of ASP was through confirmatory factor analysis, group differentiation, and concurrent validity. Confirmatory factor analysis of the ten dimensions and 73 items did not produce a good fit of the model. The model was re-specified and resulted in a hypothesized model of 14 observed variables and five latent constructs, baby, labor, marital, attractive, and support. The overall goodness-of-fit for this model was 70.41 with 67 degrees of freedom (p = .364). Group differentiation was assessed through the variables of: trimester, gravidity, age, and health during pregnancy. The mean differences between the group variables and the subscales of ASP supported previous research in the domain of pregnancy and anxiety. Concurrent validity was demonstrated through the strong correlations between the scores of ASP and the State and Trait Anxiety Inventory, as well as ASP and the State Self-Esteem Scale, which produced, as expected, a strong negative correlation. The results from this study indicate that the ASP is a valid measure of five different dimensions relating to pregnancy and those dimensions are based on an extensive review of the literature.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".