Nursing Treatment Matches the Rasch Model

A research project to define the best nursing practice (while taking care of cardiovascular post-surgery patients) is focused on the measurement of their progress 24, 48, and 72-92 hours after leaving the intensive care unit.

These measures are expected to discriminate between traits resulting from the natural improvement of the health status of the patient and other traits that are not improving and that require special nursing care or medical treatment.

A 44-item questionnaire including two or three categories has been developed in Colombia and has been administered to approximately 250 patients over a one-year term. This questionnaire includes 23 items of clinical events in the main body systems (neurological, cardiovascular, respiratory, and skin, among others) and 21 diagnosis items (mainly regarding urine and blood laboratory results and X-Rays). The nurse administers the questionnaire at three different time-points in order to study the progress of a patient. On the two- or three-category rating scale (from "low to high" or "poor to good" health condition) lower categories indicate poor condition of the patient, while higher categories indicate normal health conditions.

Analysis proceeds in tow stages: (a) improvement of the health status of the patient from 0 to 96 hours (the measured trait should show higher values at the end of the period), and (b) identifying critical variables where this progress does not occur or when an irregular condition is found, requiring the intervention of a nurse to help the patient reach a better health level.

Plots showing the model and empirical ICCs were used. The results for three items at the three different time-points are shown. The red continuous line is the Rasch-model prediction. The blue line with x's are the empirical patient statuses. The thinner grey lines are confidence intervals around the model predicted line.

Item 1 - "Conscious level of the patient": The patients show positive progress from A to B to C (x's ascending left-to-right) following the Rasch model prediction very closely. The lowest conscious level takes place during time-point A, higher at time-point B, and all patients are in the best condition at time-point C. For treatment related to this item, the participation of a nurse is only required to check the condition of the patient.

Item 2 - "Sleep and rest": At time-point A, about 98% of the patients show an irregular condition of sleep and rest; at time-point B (x's very low), 77% of the patients continue experiencing trouble. Misfitting x's can be seen in the patients with lower measures. These indicate an unexpectedly healthy condition on this item, so worth investigating further. At time-point C, 49% of the patients show no noticeable improvement with regard to time-point B (although no irregularity is found yet). Treatment related to this item requires the nurse to help the patient, if necessary, after 48 hours.

Item 8 - "Blood pressure": This is critical to the well-being of the patients, but the progress of the patients does not reach the regular level during the whole period. Irregularity in time-points B and C, as well as no significant changes in the measures, show that blood pressure should be closely supervised by the nurse and even by the physician from the start. Misfit (x's below expectation) identifies patients who have a deficit in blood pressure. Clinical intervention is needed.

Agustin Tristan (Instituto de Evaluación e Ingeniería Avanzada, S.C., Mexico), Claudia Ariza, Doctorate candidate, and Maria Mercedes Duran, Ph.D., Universidad Nacional de Colombia.

Nursing Treatment Matches the Rasch Model … A. Tristan, C. Ariza, M.M. Duran, Rasch Measurement Transactions, 2008, 21:4 p. 1142-3

Rasch-Related Resources: Rasch Measurement YouTube Channel
Rasch Measurement Transactions & Rasch Measurement research papers - free An Introduction to the Rasch Model with Examples in R (eRm, etc.), Debelak, Strobl, Zeigenfuse Rasch Measurement Theory Analysis in R, Wind, Hua Applying the Rasch Model in Social Sciences Using R, Lamprianou El modelo métrico de Rasch: Fundamentación, implementación e interpretación de la medida en ciencias sociales (Spanish Edition), Manuel González-Montesinos M.
Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar Probabilistic Models for Some Intelligence and Attainment Tests, Georg Rasch Rasch Models for Measurement, David Andrich Constructing Measures, Mark Wilson Best Test Design - free, Wright & Stone
Rating Scale Analysis - free, Wright & Masters
Virtual Standard Setting: Setting Cut Scores, Charalambos Kollias Diseño de Mejores Pruebas - free, Spanish Best Test Design A Course in Rasch Measurement Theory, Andrich, Marais Rasch Models in Health, Christensen, Kreiner, Mesba Multivariate and Mixture Distribution Rasch Models, von Davier, Carstensen
Rasch Books and Publications: Winsteps and Facets
Applying the Rasch Model (Winsteps, Facets) 4th Ed., Bond, Yan, Heene Advances in Rasch Analyses in the Human Sciences (Winsteps, Facets) 1st Ed., Boone, Staver Advances in Applications of Rasch Measurement in Science Education, X. Liu & W. J. Boone Rasch Analysis in the Human Sciences (Winsteps) Boone, Staver, Yale Appliquer le modèle de Rasch: Défis et pistes de solution (Winsteps) E. Dionne, S. Béland
Introduction to Many-Facet Rasch Measurement (Facets), Thomas Eckes Rasch Models for Solving Measurement Problems (Facets), George Engelhard, Jr. & Jue Wang Statistical Analyses for Language Testers (Facets), Rita Green Invariant Measurement with Raters and Rating Scales: Rasch Models for Rater-Mediated Assessments (Facets), George Engelhard, Jr. & Stefanie Wind Aplicação do Modelo de Rasch (Português), de Bond, Trevor G., Fox, Christine M
Exploring Rating Scale Functioning for Survey Research (R, Facets), Stefanie Wind Rasch Measurement: Applications, Khine Winsteps Tutorials - free
Facets Tutorials - free
Many-Facet Rasch Measurement (Facets) - free, J.M. Linacre Fairness, Justice and Language Assessment (Winsteps, Facets), McNamara, Knoch, Fan

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