Prospective memory, executive functions and metacognitive control: An empirical approach to their structural interrelations in young elementary school children

Introduction Prospective memory (PM), the ability to remember to perform intended activities in the future (Kliegel & Jäger, 2007), is crucial to succeed in everyday life. PM seems to improve gradually over the childhood years (Zimmermann & Meier, 2006), but yet little is known about PM competences in young school children in general, and even less is known about factors influencing its development. Currently, a number of studies suggest that executive functions (EF) are potentially influencing processes (Ford, Driscoll, Shum & Macaulay, 2012; Mahy & Moses, 2011). Additionally, metacognitive processes (MC: monitoring and control) are assumed to be involved while optimizing one’s performance (Krebs & Roebers, 2010; 2012; Roebers, Schmid, & Roderer, 2009). Yet, the relations between PM, EF and MC remain relatively unspecified. We intend to empirically examine the structural relations between these constructs. Method A cross-sectional study including 119 2nd graders (mage = 95.03, sdage = 4.82) will be presented. Participants (n = 68 girls) completed three EF tasks (stroop, updating, shifting), a computerised event-based PM task and a MC spelling task. The latent variables PM, EF and MC that were represented by manifest variables deriving from the conducted tasks, were interrelated by structural equation modelling. Results Analyses revealed clear associations between the three cognitive constructs PM, EF and MC (rpm-EF = .45, rpm-MC = .23, ref-MC = .20). A three factor model, as opposed to one or two factor models, appeared to fit excellently to the data (chi2(17, 119) = 18.86, p = .34, remsea = .030, cfi = .990, tli = .978). Discussion The results indicate that already in young elementary school children, PM, EF and MC are empirically well distinguishable, but nevertheless substantially interrelated. PM and EF seem to share a substantial amount of variance while for MC, more unique processes may be assumed.

0 and Metacognition: Relations and individual differences in 2nd graders

Introduction. Prospective Memory (PM), defined as the ability to remember to perform intended activities at some point in the future (Kliegel & Jäger, 2007), is crucial to succeed in everyday life. PM seems to increase over the childhood years (Zimmermann & Meier, 2006), but yet little is known about PM competences in children in general, but also about factors that influence its development. Currently, a number of studies has focused on factors that might influence PM performance, with EF being potentially influencing mechanisms (Ford, Driscoll, Shum & Macaulay, 2012; Mahy & Moses, 2011). Also metacognitive processes (MC: monitoring and control) are assumed to be involved while learning or optimizing one’s performance (Krebs & Roebers, 2010; 2012; Roebers, Schmid, & Roderer, 2009). Yet, the empirical relation between PM, EF and MC remains rather unclear. We intend to examine relations and explain individual differences in PM performance. Method. An empirical cross-sectional study on 120 2nd graders will be presented. Participants completed six EF tasks (a Stroop, two Updating Tasks, two Shifting Tasks, a Flanker Task), a computerised event-based PM Task and a MC spelling task. Children were tested individually in two sessions of 30 minutes each. Each of the three EF components defined by Miyake, Friedman, Emerson, Witzki & Howerter (2002) was represented by two variables. PM performance was represented by PM accuracy. Metacognitive processes (control, monitoring) were represented separately. Results. Preliminary analyses (SEM) indicate a substantial association between EF (updating, inhibition) and PM. Further, MC seems to be significantly related only to EF. We will explore whether metacognitive monitoring is related to PM monitoring (Roebers, 2002; Mantylä, 2007). As to EF and MC, we expect the two domains to be empirically well distinguishable and nevertheless substantially interrelated. Discussion. The results are discussed on a broader and interindividual level.

External validation of the Hospital-patient One-year Mortality Risk (HOMR) model for predicting death within 1 year after hospital admission

BACKGROUND Predicting long-term survival after admission to hospital is helpful for clinical, administrative and research purposes. The Hospital-patient One-year Mortality Risk (HOMR) model was derived and internally validated to predict the risk of death within 1 year after admission. We conducted an external validation of the model in a large multicentre study. METHODS We used administrative data for all nonpsychiatric admissions of adult patients to hospitals in the provinces of Ontario (2003-2010) and Alberta (2011-2012), and to the Brigham and Women's Hospital in Boston (2010-2012) to calculate each patient's HOMR score at admission. The HOMR score is based on a set of parameters that captures patient demographics, health burden and severity of acute illness. We determined patient status (alive or dead) 1 year after admission using population-based registries. RESULTS The 3 validation cohorts (n = 2,862,996 in Ontario, 210 595 in Alberta and 66,683 in Boston) were distinct from each other and from the derivation cohort. The overall risk of death within 1 year after admission was 8.7% (95% confidence interval [CI] 8.7% to 8.8%). The HOMR score was strongly and significantly associated with risk of death in all populations and was highly discriminative, with a C statistic ranging from 0.89 (95% CI 0.87 to 0.91) to 0.92 (95% CI 0.91 to 0.92). Observed and expected outcome risks were similar (median absolute difference in percent dying in 1 yr 0.3%, interquartile range 0.05%-2.5%). INTERPRETATION The HOMR score, calculated using routinely collected administrative data, accurately predicted the risk of death among adult patients within 1 year after admission to hospital for nonpsychiatric indications. Similar performance was seen when the score was used in geographically and temporally diverse populations. The HOMR model can be used for risk adjustment in analyses of health administrative data to predict long-term survival among hospital patients.