Adaptation and psychometric assessment of the postpartum childcare stress checklist among Arab women in the United Arab Emirates
Bibliographic record
Abstract
PROBLEM: There is currently no validated version of the Postpartum Childcare Stress Checklist (PCSC) in the context of Arabic-speaking women in the United Arab Emirates (UAE). BACKGROUND: The postpartum period is a transformative phase in a woman's life, during which mothers are highly vulnerable to mental health problems, compounded by childcare demands. Childcare stress is a strong predictor of postpartum depression. Thus, there is a need for a validated Arabic measure of childcare stress. AIM: This study aims to translate and psychometrically evaluate the Arabic version of the PCSC among mothers residing in the UAE. METHODS: The PCSC was psychometrically assessed in a cohort of 399 women in the UAE between February 2017 and September 2018. Using best practices in instrument translation; psychometric evaluation included item-test and item-total correlations, confirmatory factor analysis, tests at two-time points, and validity tests. RESULTS: The analysis produced a 19-item scale in both unidimensional and multidimensional formats, with Geomin rotated factor loadings ranging from 0.31 to 0.84 and an average variance of 0.46, demonstrating adequate validity. The Arabic PCSC had acceptable reliability coefficients at three (α= = 0.79) and six (α=0.80) months and adequate concurrent and predictive validity at three and six months postpartum. DISCUSSION: Childcare stress is an internationally reliable and validated construct. The PCSC was also positively correlated with depressive and anxiety symptoms. CONCLUSION: The Arabic PCSC is a psychometrically validated measure which can be used in future Arabic-language studies and may lead to improved interventions for maternal mental health across Arab-speaking communities.
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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 | 0.003 | 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 | 0.000 |
| 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".