Sentence examples for classifying errors from inspiring English sources

6 Research into causes of medication problems has tended to focus on identifying and classifying errors in the medication management process.

Information and Display Systems runs video training sessions with its statisticians to work toward as much unanimity as possible in classifying errors, but making it an exact science may be impossible.

All prescriptions were reviewed a third time by one of the investigators (KM), to make sure that there was agreement in classifying errors.

This research introduces an algorithm for classifying error types of front-line workers involved in occupational incidents, based on the skill rule knowledge (SRK) framework.

We then propose a taxonomy for classifying error-handling strategies that has the following three dimensions: the main actor in the error-handling process (machine versus user), the purpose of the strategy (error prevention, discovery, or correction), and the use of different modalities of interaction.

Important limitations of this study are that it is a non-randomised study conducted at a single site, the subjective nature of determining and classifying error types such as legibility, the FormularyScript did not include all necessary medications and physicians were aware that the prescriptions were being analysed for prescribing errors.

The Federal Aviation Administration, which is investigating, classifies errors at radar control centers on an A-through-D scale, with A the most serious.

This research uses action, process, object, and schema (APOS) theory to classify errors into categories and to analyse and interpret the data collected.

This study used Dubinsky's (1991) APOS Theory (Actions, Processes, Objects, and Schemas) to classify errors into categories and analyse the data collected.

The remaining missing reads are composed of a large number of classes of sequence tag sequences each containing small numbers of errors including additional 5' bases, misincorporated bases or difficult to classify errors that presumably arose during the PCR amplification.

To quantify this tendency, we classified errors on inbound trials according to rat's choice of destination: trials on which the rat ran from one side food-well to the opposite side (while skipping the center arm) were classified as side-to-side errors; trials on which the rat returned to the outside arm food-well from which it had just departed were classified as turn-around errors.