Challenges in applying the short Childbirth Self-Efficacy Inventory (CBSEI-C32) in German
Bibliographic record
Abstract
INTRODUCTION: This study aimed to review and pilot-test feedback from childbearing women who completed the German short version of the Childbirth Self-Efficacy Inventory (CBSEI-C32), which is widely used and validated in different languages. METHODS: Ten pregnant nulliparas, who planned a natural childbirth, completed the German CBSEI-C32 and provided comments about the comprehensibility of the tool. RESULTS: When applying the standardized translated German CBSEI-C32, we discovered that women generally gave positive feedback, and reported that the items made them think about coping strategies for labor and birth. Some pregnant woman had problems in understanding two items: 'Mich beherrschen' (original English item: 'Keep myself in control'), and 'Mich ruhig halten' (original English item: 'Keep myself calm'). Some of the items were not comprehensible for pregnant women and might not represent contemporary concepts of childbirth self-efficacy. CONCLUSIONS: Two items of the German CBSEI-C32 were interpreted ambiguously by the pilot testers. The CBSEI should be checked to identify which items could serve as the basis for a new questionnaire because there are clear and appropriate coping strategies when dealing with labour pain such as item 3 on breathing. These could be complemented with other coping behaviours that are positively worded and serve to empower rather than restrain women. For measuring self-efficacy beliefs in childbirth nowadays, it appears that health-oriented aspects, such as concentrating on the pauses between contractions or mentally staying in the present moment, are more important for women than focusing on control during childbirth.
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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 | 0.051 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".