This paper proposes and showcases a methodology to develop an observational behavior assessment instrument to assess psychological competencies of police officers. We outline a step-by-step methodology for police organizations to measure and evaluate behavior in a meaningful way to assess these competencies. We illustrate the proposed methodology with a practical example. We posit that direct behavioral observation can be key in measuring the expression of psychological competence in practice, and that psychological competence in practice is what police organizations should care about. We hope this paper offers police organizations a methodology to perform scientifically informed observational behavior assessment of their police officers’ psychological competencies and inspires additional research efforts into this important area.
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Steeds meer gemeenten hanteren in hun beleidsplannen als doelstelling van schulddienstverlening 'iedereen het maximaal haalbare' in plaats van 'iedereen schuldenvrij'. Dit vraagt dat het 'maximaal haalbare' tijdens de intake wordt vastgesteld. Gedrag, motivatie en vaardigheden zijn daarbij cruciaal. Daarom is inzicht in die aspecten noodzakelijk. Om dat inzicht op een gestructureerde manier tot stand te brengen is het methodisch screeningsinstrument schulddienstverlening (Mesis©) geconstrueerd. In dit artikel worden in kort bestek inhoud, functie, doel en werkwijze van Mesis© uiteengezet
AIM: This paper is a report of the development and testing of the psychometric properties of an instrument to measure the accuracy of nursing documentation in general hospitals.BACKGROUND: Little information is available about the accuracy of nursing documentation. None of the existing instruments that quantify accuracy of nursing diagnoses, interventions, and progress and outcome evaluations are suitable to measure documentation in general hospital environments, nor were they intended for this purpose.METHOD: The D-Catch instrument, based on the Cat-ch-Ing instrument and the Scale for Degrees of Accuracy in Nursing Diagnoses, was developed in 2007-2008. Content validity of the D-Catch instrument was assessed by two Delphi panels, in which pairs of independent reviewers assessed 245 patient records in seven hospitals in the Netherlands. Construct validity was assessed by explorative factor analysis with principal components and varimax rotation. Internal consistency was measured by Cronbach's alpha. The inter-rater reliability of the D-Catch instrument was tested by calculating Cohen's weighted kappa (K(w)) for each pair of reviewers. Results. Quantity and quality variables were used to assess the accuracy of nursing documentation. Three constructs were identified in the factor analysis. 'Accuracy of the nursing diagnosis' was the only variable with substantial loading on component two (0.907) and a modest loading on component one (0.230). Internal consistency (Cronbach's alpha) was 0.722. The inter-rater reliability (K(w)) varied between 0.742 and 0.896.CONCLUSION: The D-Catch instrument is a valid and reliable measurement instrument to assess nursing documentation in general hospital settings.
Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
Democratie, burgerschapsvorming, kritisch denken en Bildung worden vaak samen genoemd, maar een heldere kijk op de onderlinge samenhang ontbreekt nog. In dit onderzoeksproject ontwikkelen we een visie op burgerschapsvorming in het middelbaar beroepsonderwijs, waarin kritisch denken en Bildung worden opgenomen.
