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 Measurement Transactions (free, online)||Rasch Measurement research papers (free, online)||Probabilistic Models for Some Intelligence and Attainment Tests, Georg Rasch||Applying the Rasch Model 3rd. Ed., Bond & Fox||Best Test Design, Wright & Stone|
|Rating Scale Analysis, Wright & Masters||Introduction to Rasch Measurement, E. Smith & R. Smith||Introduction to Many-Facet Rasch Measurement, Thomas Eckes||Invariant Measurement: Using Rasch Models in the Social, Behavioral, and Health Sciences, George Engelhard, Jr.||Statistical Analyses for Language Testers, Rita Green|
|Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar||Journal of Applied Measurement||Rasch models for measurement, David Andrich||Constructing Measures, Mark Wilson||Rasch Analysis in the Human Sciences, Boone, Stave, Yale|
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