Dataset Information

Identifying vulnerable mother-infant dyads: a psychometric evaluation of two observational coding systems using varying interaction periods.

ABSTRACT:

Introduction

Clinical services require feasible assessments of parent-infant interaction in order to identify dyads requiring parenting intervention. We assessed the reliability and predictive validity of two observational tools and tested whether briefer forms could be identified which retain acceptable psychometric properties over short observation periods.

Methods

A stratified high-risk community sample of 250 mother-infant dyads from The Wirral Child Health and Development Study completed 7-min play-based interaction at 6-8 months. Film-footage was independently coded by two trained raters using PIIOS and NICHD-SECCYD systems. Incremental predictive validity was assessed from 3, 5 and 7 min observation to attachment outcomes (Strange Situation; 14 months) and infant mental health (BITSEA; 14 and 30 months).

Results

Excellent inter-rater reliability was evident at code and subscale level for each tool and observation period. Stability of within-rater agreement was optimal after 5 min observation. ROC analysis confirmed predictive (discriminant) validity (AUCs >0.70) to top decile age 2 mental health outcomes for PIIOS total score and a brief 3-item composite from NICHD-SECCYD (sensitivity, intrusiveness, positive regard; NICHD-3), but not to attachment outcomes. Logistic regression showed dyads rated at-risk for externalizing problems using NICHD-3 were also at significantly higher risk for insecurity at 14 months (OR = 2.7, p = 0.004).

Conclusion

PIIOS total and NICHD-3 ratings from 5 min observation are both reliable and valid tools for use in clinical practice. Findings suggest NICHD-3 may have greater utility due to its comparative brevity to train and code, with suitability for use over a broader developmental time frame (3-24 months).

SUBMITTER: Sharp H

PROVIDER: S-EPMC11233099 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identifying vulnerable mother-infant dyads: a psychometric evaluation of two observational coding systems using varying interaction periods.

Sharp Helen H Vitoratou Silia S O'Mahen Heather H Bozicevic Laura L Refberg Miriam M Hayes Chloe C Gay Jessica J Pickles Andrew A

Frontiers in psychology 20240624

<h4>Introduction</h4>Clinical services require feasible assessments of parent-infant interaction in order to identify dyads requiring parenting intervention. We assessed the reliability and predictive validity of two observational tools and tested whether briefer forms could be identified which retain acceptable psychometric properties over short observation periods.<h4>Methods</h4>A stratified high-risk community sample of 250 mother-infant dyads from The Wirral Child Health and Development Stu ...[more]

PMID: 38984279

Similar Datasets

Project description:IntroductionIn low-income country settings, the first six weeks after birth remain a critical period of vulnerability for both mother and newborn. Despite recommendations for routine follow-up after delivery and facility discharge, few mothers and newborns receive guideline recommended care during this period. Prediction modelling of post-delivery outcomes has the potential to improve outcomes for both mother and newborn by identifying high-risk dyads, improving risk communication, and informing a patient-centered approach to postnatal care interventions. This study aims to derive post-discharge risk prediction algorithms that identify mother-newborn dyads who are at risk of re-admission or death in the first six weeks after delivery at a health facility.MethodsThis prospective observational study will enroll 7,000 mother-newborn dyads from two regional referral hospitals in southwestern and eastern Uganda. Women and adolescent girls aged 12 and above delivering singletons and twins at the study hospitals will be eligible to participate. Candidate predictor variables will be collected prospectively by research nurses. Outcomes will be captured six weeks following delivery through a follow-up phone call, or an in-person visit if not reachable by phone. Two separate sets of prediction models will be built, one set of models for newborn outcomes and one set for maternal outcomes. Derivation of models will be based on optimization of the area under the receiver operator curve (AUROC) and specificity using an elastic net regression modelling approach. Internal validation will be conducted using 10-fold cross-validation. Our focus will be on the development of parsimonious models (5-10 predictor variables) with high sensitivity (>80%). AUROC, sensitivity, and specificity will be reported for each model, along with positive and negative predictive values.DiscussionThe current recommendations for routine postnatal care are largely absent of benefit to most mothers and newborns due to poor adherence. Data-driven improvements to postnatal care can facilitate a more patient-centered approach to such care. Increasing digitization of facility care across low-income settings can further facilitate the integration of prediction algorithms as decision support tools for routine care, leading to improved quality and efficiency. Such strategies are urgently required to improve newborn and maternal postnatal outcomes.Clinical trial registrationhttps://clinicaltrials.gov/, identifier (NCT05730387).

Dataset Information

Identifying vulnerable mother-infant dyads: a psychometric evaluation of two observational coding systems using varying interaction periods.

Introduction

Methods

Results

Conclusion

Publications

Identifying vulnerable mother-infant dyads: a psychometric evaluation of two observational coding systems using varying interaction periods.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets