Validation of the Wijma delivery expectancy/experience questionnaire for pregnant women in Malawi: a descriptive, cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Fear of childbirth is a common health concern for women during pregnancy. The Wijma Delivery Expectancy/Experience Questionnaire (W-DEQ) is widely used to measure childbirth fear during pregnancy. However, this instrument is yet to be validated in Malawi, Africa. Our study aimed to assess the psychometric properties of the W-DEQ questionnaire in Malawi. METHODS: Healthy pregnant women in the third trimester (N = 476) at a district hospital in Malawi were included. Fear of childbirth, depressive symptoms, and quality of life were assessed using the W-DEQ, the Edinburg Postnatal Depression Scale, and the World Health Organization Quality of Life scale, respectively. The construct validity, reliability, and convergent validity of the W-DEQ were examined using exploratory and confirmatory factor analyses, Cronbach's alpha, and Pearson correlations. RESULTS: The mean age of participants was 28.2 (standard deviation = 6.8) years. Exploratory and confirmatory factor analysis of the Malawian version of the W-DEQ indicated a multidimensional structure with three factors: fear, negative appraisal, and a lack of self-efficacy, with acceptable goodness of model fit. The Malawian version of the W-DEQ showed a satisfactory internal consistency (α = 0.84) and was significantly correlated with depressive symptoms (r = 0.23, p < 0.001) and quality of life (r = - 0.17 ~ -0.26, ps < 0.05). CONCLUSIONS: Our findings support the Malawian W-DEQ version being a reliable and valid instrument for measuring childbirth fear in African women.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".