Development of a revised and abbreviated version of the postpartum bonding questionnaire (PBQ‐R): First U.S. validation and association to child outcomes
Bibliographic record
Abstract
The postpartum bonding questionnaire (PBQ) is a maternal-reported 25-item measure of bonding, available in 15 languages, and widely used for clinical and research purposes in the United States (U.S.) and across the globe. Nonetheless, its putative 4-factor structure initially proposed in 2001 has never generalized or been replicated in other samples, nor has it been studied in U.S. populations. Using a U.S.-based sample of 610 English-speaking mothers who completed the PBQ 4 months postpartum-mean 32.51 ± 5.25 years old and 47.5% first-time mothers-the initial goal of this study was to confirm the 4-factor/25-item structure of the PBQ. Aligned with other published studies, our confirmatory factor analysis did not support the 4-factor/25-item structure. We then used exploratory factor analysis which supported the creation of a 1-factor/14-item abbreviated measure, the PBQ-R. Unlike previous versions of the PBQ, the PBQ-R is scored so that higher scores indicate stronger bonding. The validity of the PBQ-R was supported by its high internal consistency in this sample (w = 0.89), and correlations with maternal depression (ρ = -0.46) and child neurodevelopment (ρ = 0.11 to ρ = 22) and socio-emotional symptoms (ρ = -0.22 to ρ = -0.33). The new unidimensional shorter PBQ-R is suitable for use in the U.S. as a measure of general mother-infant bonding.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".