School readiness and social determinants of health: A collaboration with community teachers and parents.

PURPOSE: To explore perceptions of community dwelling caregivers of preschool-aged children regarding the influence of social determinants of health (SDOH) on children's school readiness. Parents' perspectives regarding solutions to enhance school readiness in preschool-aged children are also explored. METHODS: This study employed a qualitative, descriptive design and a community based participatory research (CBPR) approach. An academic institution collaborated with parents, teachers, and administrators at a community-based preschool learning center. Ten young-adult to middle-aged mothers and caregivers attended two separate focus groups and completed open-ended questionnaires. Inductive and deductive thematic analysis of text were employed. FINDINGS: Three themes emerged 1) Families described the vast lack of appropriate community resources and inability to access those resources that are available to prepare their children for school 2). Family members need help processing information about social resources 3) Community, individual and systemic level solutions to enhance school readiness. CONCLUSIONS: Academic-Community partnerships provide an opportunity to (1) identify solutions to remove systemic barriers that impede children's readiness for school, and (2) design interventions to support families through that process. Interventions to enhance school readiness should be family-focused and can be informed by understanding the influence of SDOH during the planning stages. SDOH create barriers and prevent parents from prioritizing their children's school, health-care, and developmental needs. PRACTICE IMPLICATIONS: Interventions to enhance school readiness should be family-based and can be informed by understanding the influence of SDOH during the planning stages. Social advocacy is also needed to enhance the ability of parents to enhance their children's school readiness.

Longitudinal stability of IRT and equivalent-groups linear and equipercentile equating.

A multiscale criterion-referenced test that featured two presumably equivalent forms (A and B), was administered to 1,667 Head Start children at each of four points over an academic year. Using a randomly equivalent groups design, three equating methods were applied: common-item IRT equating using concurrent calibration, linear transformation, and equipercentile transformation. The methods were compared by examining mean score differences, weighted mean squared difference, and Kolmogorov's D statistics for each subscale. The results indicated that over time the IRT equating method and conventional equating methods exhibited different patterns of discrepancy between the two test forms. IRT equating yielded marginally smaller form-to-form mean score differences and generated slightly fewer distributional discrepancies between Forms A and B than both linear and equipercentile equating. However, the results were mixed indicating that more studies are needed to provide additional information on the relative merits and weaknesses of each approach.

Measuring preschool cognitive growth while it's still happening: the Learning Express.

Educators need accurate assessments of preschool cognitive growth to guide curriculum design, evaluation, and timely modification of their instructional programs. But available tests do not provide content breadth or growth sensitivity over brief intervals. This article details evidence for a multiform, multiscale test criterion-referenced to national standards for alphabet knowledge, vocabulary, listening comprehension and mathematics, developed in field trials with 3433 3-5(1/2)-year-old Head Start children. The test enables repeated assessments (20-30 min per time point) over a school year. Each subscale is calibrated to yield scaled scores based on item response theory and Bayesian estimation of ability. Multilevel modeling shows that nearly all score variation is associated with child performance rather than examiner performance and individual growth-curve modeling demonstrates the high sensitivity of scores to child growth, controlled for age, sex, prior schooling, and language and special needs status.