Development, Psychometric Assessment, and Predictive Validity of the Postpartum Childcare Stress Checklist
Bibliographic record
Abstract
BACKGROUND: Childcare stress has been shown to predict postpartum depression; however, there is little research exploring and validating the dimensions of childcare stress instruments such that preventive interventions can be created. OBJECTIVES: The aim of this study was to develop and psychometrically test an instrument to measure parental perceptions of postpartum childcare stress. METHODS: Using research based on postpartum stress and childcare stress, the Postpartum Childcare Stress Checklist (PCSC) was developed, and content validity was judged by experts. The PCSC was psychometrically assessed in a cohort of 541 women in a health region near Vancouver, Canada, who were followed to 8 weeks postpartum in 2002. The psychometric assessment analyses comprised internal consistency, exploratory factory analysis, concurrent validity, and predictive validity. RESULTS: The 19-item PCSC had good internal consistency (Kuder-Richardson Formula 20 coefficient: 0.81). Exploratory factor analysis revealed the following dimensions: (a) relationship with the partner, (b) caring for the infant, (c) maternal social interactions, and (d) establishing a new routine. Predictive validity analyses showed that PCSC total and subscale scores at 4 weeks were positively correlated with depressive symptomatology, anxiety, and perceived stress and negatively correlated with global and partner support at 8 weeks postpartum. DISCUSSION: The PCSC is a measure of childcare stress with excellent reliability and validity. Upon further testing, it may be used to identify women and couples in need of greater support, individualize postpartum care, and evaluate the effectiveness of preventive interventions.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".