The measurement properties and acceptability of a new parent-infant bonding tool (Me and My Baby) designed for use in a universal healthcare setting in the UK

NICE guidelines acknowledge the importance of the parent-infant relationship for child development but highlight the need for further research to establish reliable tools for assessment, particularly for parents of children under one year. This study explores the acceptability and psychometric properties of a co-developed tool, Me and My Baby (MaMB). Study design A cross-sectional design was applied. The MaMB was administered universally (in two sites) 27 with mothers during routine 6 to 8 week Health Visitor contacts. The sample comprised 467 mothers (434 MaMB completers and 33 non-completers). Dimensionality of instrument responses were evaluated via exploratory and confirmatory ordinal factor analyses. Item response modelling was conducted via a Rasch calibration to evaluate how the tool conformed to principles of fundamental measurement. Tool acceptability was evaluated via completion rates and comparing completers and non-completers demographic differences on age, parity, ethnicity, and English as an additional language. Free-text comments were summarised. Data sharing agreements and data management were compliant with the General Data Protection Regulation, and University of York data management policies. Results High completion rates suggested the MaMB was acceptable. Psychometric analyses showed the response data to be an excellent fit to a unidimensional confirmatory factor analytic model. All items loaded statistically significantly and substantially (>0.4) on a single underlying factor (latent variable). The item response modelling showed that most MaMB items fitted the Rasch model. Item reliability was high (0.94) yet the test yielded little information on each respondent, Me and My Baby (MaMB) as highlighted by the person separation index of 0.1 (=>2.0 is required to reliably discriminate between two groups). Conclusions and next steps MaMB reliably measures a single construct, likely to be infant bonding. However, further validation work is needed, preferably with enriched population samples to include higher- need/risk families. The MaMB tool may benefit from reduced response categories (from four to three) and some modest item wording amendments. Following further validation and reliability appraisal the MaMB may ultimately be used with fathers/other primary caregivers and be potentially useful in research, universal health settings as part of a referral pathway, and clinical practice, to identify dyads in need of additional support/interventions.

Photosynthetic heat tolerance in wheat: Evidence for genotype-by-environment interactions

High temperature stress inhibits wheat photosynthetic processes and threatens wheat production. Photosynthetic heat tolerance (commonly measured as T – the critical temperature at which incipient damage to photosystem II occurs) in wheat genotypes could be improved by exploiting genetic variation and genotype-by-environment interaction (GEI) for this trait. Flag leaf T of a total of 54 wheat genotypes were evaluated in 12 thermal environments over three years in Australia using linear mixed models for assessing GEI effects. Nine of the 12 environments had significant genotypic effect and highly variable broad-sense heritability (H ranged from 0.15 to 0.75). T GEI was variable, with 55.6% of the genetic variance across environments accounted for by the factor analytic model. Mean daily growth temperature preceding anthesis was the most influential environmental driver of T GEI, suggesting varied scales of biochemical, physiological, and structural adaptations to temperature requiring different durations to manifest at the thylakoid membrane and leaf levels. These changes help protect or repair photosystem II upon exposure to heat stress. To support current wheat breeding, we identified genotypes superior to commercial cultivars commonly grown by farmers, and showed that there is potential for developing genotypes with greater photosynthetic heat tolerance.

Is Adequate Model Fit Indicative of an Adequate Factor Analytic Model? Recognizing Construct Heterogeneity and Model Misspecification in Factor Analytic Research

This study builds upon recent research indicating that focusing narrowly on model fit when evaluating factor analytic models can lead to problematic inferences regarding the nature of item sets, as well as how models should be applied to inform measure development and validation. Specifically, we demonstrate that an overreliance on model fit may lead to (a) incorrect conclusions that heterogeneous item sets reflect narrower homogeneous constructs and (b) the retention of potentially problematic items when developing assessment measures. We use both interview data from adult outpatients (N = 2,149) and self-report data from adults recruited online (N = 547) to demonstrate the importance of these issues across sample types and assessment methods. Following demonstrations with these data, we make recommendations focusing on how theory and other model characteristics (e.g., factor loading patterns) should be considered in addition to information provided by model fit indices when evaluating factor analytic models.

Meta-Analytic Structural Equation Modeling

Meta-analysis and structural equation modeling (SEM) are two popular statistical models in the social, behavioral, and management sciences. Meta-analysis summarizes research findings to provide an estimate of the average effect and its heterogeneity. When there is moderate to high heterogeneity, moderators such as study characteristics may be used to explain the heterogeneity in the data. On the other hand, SEM includes several special cases, including the general linear model, path model, and confirmatory factor analytic model. SEM allows researchers to test hypothetical models with empirical data. Meta-analytic structural equation modeling (MASEM) is a statistical approach combining the advantages of both meta-analysis and SEM for fitting structural equation models on a pool of correlation matrices. There are usually two stages in the analyses. In the first stage of analysis, a pool of correlation matrices is combined to form an average correlation matrix. In the second stage of analysis, proposed structural equation models are tested against the average correlation matrix. MASEM enables researchers to synthesize researching findings using SEM as the research tool in primary studies. There are several popular approaches to conduct MASEM, including the univariate-r, generalized least squares, two-stage SEM (TSSEM), and one-stage MASEM (OSMASEM). MASEM helps to answer the following key research questions: (a) Are the correlation matrices homogeneous? (b) Do the proposed models fit the data? (c) Are there moderators that can be used to explain the heterogeneity of the correlation matrices? The MASEM framework has also been expanded to analyze large datasets or big data with or without the raw data.

Examining the Short Dark Tetrad (SD4) Across Models, Correlates, and Gender

To date, no studies have examined a range of structural models of the interpersonally aversive traits tapped by the Short Dark Tetrad (SD4; narcissism, Machiavellianism, psychopathy, sadism), in conjunction with their measurement invariance (males vs. females) and how the models each predict external correlates. Using a large sample of young adults ( N = 3,975), four latent variable models were compared in terms of fit, measurement invariance, and prediction of intrapersonal and interpersonal functioning. The models tested were as follows: (Model A) confirmatory factor analytic, (Model B) bifactor, (Model C) exploratory structural equation model, and (Model D) a reduced-item confirmatory factor analytic that maximized item information. All models accounted for item covariance with good precision, although differed in incremental fit. Strong invariance held for all models, and each accounted similarly for the external correlates, highlighting differential predictive effects of the SD4 factors. The results provide support for four theoretically distinct but overlapping dark personality domains.

Bullying among adolescents: The role of skills

Bullying cannot be tolerated as a normal social behavior portraying a child's life. This paper quantifies its negative consequences allowing for the possibility that victims and nonvictims differ in unobservable characteristics. To this end, we introduce a factor analytic model for identifying treatment effects of bullying in which latent cognitive and noncognitive skills determine victimization and multiple outcomes. We use early test scores to identify the distribution of these skills. Individual‐, classroom‐ and district‐level variables are also accounted for. Applying our method to longitudinal data from South Korea, we first show that while noncognitive skills reduce the chances of being bullied during middle school, the probability of being victimized is greater in classrooms with relatively high concentration of boys, previously self‐assessed bullies and students that come from violent families. We report bullying at age 15 has negative effects on physical and mental health outcomes at age 18. We also uncover heterogeneous effects by latent skills, from which we document positive effects on the take‐up of risky behaviors and negative effects on schooling attainment. Our findings suggest that investing in noncognitive development should guide policy efforts intended to deter this problematic behavior.

Development and validation of a scale evaluating pregnant women's breastfeeding competency in third trimester: Breastfeeding Competency Scale(BCS)

Background: Breastfeeding plays an important role in the early stages of humans and throughout the development process. Breastfeeding competency is a self-assessment of pregnant women's overall competency to breastfeeding which could predict behaviors of pregnant women’ breastfeeding. However, a valid and reliable scale to assess the breastfeeding competency has not yet been developed and validated. This study was designed to develop and validate an assessment scale designed to assess the pregnant women's breastfeeding competency in third trimester: Breastfeeding Competency Scale(BCS).Methods: The BCS was developed and validated over three phases between September 2018–September 2019 which include item statistics, exploratory factor analysis(EFA), content validation, internal consistency assessment, split-half reliability and confirmatory factor analysis.Results: Item statistics and exploratory factor analysis resulted in 38 items, 4 factors that explained 66.489% of total variance. The Cronbach’s α coefficient in total scale and 4 factors were 0.970, 0.960, 0.940, 0.822, 0.931 respectively. The split-half reliability of BCS was 0.894, 0.890. Confirmatory factor analytic model showed the 4-factor model matching the data well.Conclusions: The BCS was a new instrument with certain validity and reliability for assessing the breastfeeding competency of pregnant women in third trimester.

Overlap between attention-deficit hyperactivity disorder and neurodevelopmental, externalising and internalising disorders: separating unique from general psychopathology effects

BackgroundAlthough attention-deficit hyperactivity disorder (ADHD) is classified as a neurodevelopmental disorder in the latest diagnostic manuals, it shows phenotypic and genetic associations of similar magnitudes across neurodevelopmental, externalising and internalising disorders.AimsTo investigate if ADHD is aetiologically more closely related to neurodevelopmental than externalising or internalising disorder clusters, after accounting for a general psychopathology factor.MethodFull and maternal half-sibling pairs (N = 774 416), born between 1980 and 1995, were identified from the Swedish Medical Birth and Multi-Generation Registers, and ICD diagnoses were obtained from the Swedish National Patient Register. A higher-order confirmatory factor analytic model was fitted to examine associations between ADHD and a general psychopathology factor, as well as a neurodevelopmental, externalising and internalising subfactor. Quantitative genetic modelling was performed to estimate the extent to which genetic, shared and non-shared environmental effects influenced the associations with ADHD.ResultsADHD was significantly and strongly associated with all three factors (r = 0.67–0.75). However, after controlling for a general psychopathology factor, only the association between ADHD and the neurodevelopmental-specific factor remained moderately strong (r = 0.43, 95% CI = 0.42–0.45) and was almost entirely influenced by genetic effects. In contrast, the association between ADHD and the externalising-specific factor was smaller (r = 0.25, 95% CI = 0.24–0.27), and largely influenced by non-shared environmental effects. There remained no internalising-specific factor after accounting for a general factor.ConclusionsFindings suggest that ADHD comorbidity is largely explained by genetically influenced general psychopathology, but the strong link between ADHD and other neurodevelopmental disorders is also substantially driven by unique genetic influences.