Qualitätsgesicherte Übersetzung des Wijma Delivery Expectancy Questionnaire (W-DEQ_A) ins Deutsche
Bibliographic record
Abstract
INTRODUCTION: The "Delivery Expectancy Questionnaire" by Claas Wijma et al. (W-DEQ_A), which is the most frequently used internationally to determine high levels of fear of childbirth in pregnancy, was not previously available in German. In European countries, Canada, Australia and the United States, fear of childbirth is reported to have a prevalence of 6.3 to 14.8%. Particularly, women with a fear of childbirth have an increased risk for preeclampsia, intrauterine growth retardation, and caesarean sections. METHODS: An English version of the W-DEQ_A authorized by Claas Wijma was translated and culturally adapted according to the guideline of Ohrbach et al. (INfORM). Content validity was statistically determined by means of the content validity index/average method (S-CVI/Ave). RESULTS: The translation of all text sections of the W-DEQ_A was subjected to independent appraisal. One introductory question and three items needed to be retranslated. Moreover, three items required rewording to achieve cultural equivalence. The calculated content validity yielded an "excellent" S-CVI/Ave of 0.91. CONCLUSION: The W-DEQ_A is now available in a German version for the self-assessment of fear of childbirth. It is entitled "Gedanken und Gefühle schwangerer Frauen im Hinblick auf die bevorstehende Geburt". In the form of a digital health app, the questionnaire could be prescribed and the result directly transferred to the electronic patient record.
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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 | 0.008 | 0.017 |
| 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) | 0.007 | 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".