BACKGROUND: This paper reports a study about the effect of knowledge sources, such as handbooks, an assessment format and a predefined record structure for diagnostic documentation, as well as the influence of knowledge, disposition toward critical thinking and reasoning skills, on the accuracy of nursing diagnoses.Knowledge sources can support nurses in deriving diagnoses. A nurse's disposition toward critical thinking and reasoning skills is also thought to influence the accuracy of his or her nursing diagnoses.METHOD: A randomised factorial design was used in 2008-2009 to determine the effect of knowledge sources. We used the following instruments to assess the influence of ready knowledge, disposition, and reasoning skills on the accuracy of diagnoses: (1) a knowledge inventory, (2) the California Critical Thinking Disposition Inventory, and (3) the Health Science Reasoning Test. Nurses (n = 249) were randomly assigned to one of four factorial groups, and were instructed to derive diagnoses based on an assessment interview with a simulated patient/actor.RESULTS: The use of a predefined record structure resulted in a significantly higher accuracy of nursing diagnoses. A regression analysis reveals that almost half of the variance in the accuracy of diagnoses is explained by the use of a predefined record structure, a nurse's age and the reasoning skills of `deduction' and `analysis'.CONCLUSIONS: Improving nurses' dispositions toward critical thinking and reasoning skills, and the use of a predefined record structure, improves accuracy of nursing diagnoses.
Rationale: The PG-SGA is a validated instrument to assess malnutrition and its risk factors. Its patient component, i.e. the PG-SGA Short Form (SF), can be used as screening instrument. In this multicenter study, we aimed to assess diagnostic accuracy of the PG-SGA SF and NRS 2002, in patients at the Internal Medicine ward.Methods: In 192 patients (76.0±13.5 years; 53% female) in 9 Portuguese internal medicine wards, malnutrition risk was assessed by PG-SGA SF and NRS 2002. PG-SGA SF ≤8 was defined as low/medium malnutrition risk and NRS 2002 ≤2 as low risk. PG-SGA SF ≥9 and NRS 2002 ≥3 were defined as high malnutrition risk. Nutritional status was assessed by the full PG-SGA (reference method). Malnutrition was defined as PG-SGA Stage B (moderate/suspected malnutrition) or Stage C (severely malnourished). Diagnostic accuracy was tested by sensitivity, specificity, positive and negative predictive value, and receiver operating curve. Agreement between PG-SGA and NRS-2002 was tested by McNemar’s test and Cohen’s kappa (κ).Results: Forty-six % and 53% were categorized as at risk of malnutrition by PG-SGA SF and NRS 2002, respectively. In total, 55% were malnourished. Sensitivity, specificity, positive and negative predictive value of PG-SGA SF and NRS 2002 were 0.84, 1.00, 1.00, 0.83 and 0.74, 0.74, 0.77 and 0.70, respectively. Area under curve of PG-SGA SF and NRS 2002 was 0.987 and 0.778 respectively. McNemar’s test showed no significant disagreement (p=0.86) between PG-SGA SF and NRS 2002. Cohen’s kappa showed weak agreement (κ=0.492; p<0.001) (Table 1).Conclusion: Our findings indicate that in patients at the internal medicine ward, PG-SGA SF shows better diagnostic accuracy than NRS 2002, i.e. better sensitivity and specificity.
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Various companies in diagnostic testing struggle with the same “valley of death” challenge. In order to further develop their sensing application, they rely on the technological readiness of easy and reproducible read-out systems. Photonic chips can be very sensitive sensors and can be made application-specific when coated with a properly chosen bio-functionalized layer. Here the challenge lies in the optical coupling of the active components (light source and detector) to the (disposable) photonic sensor chip. For the technology to be commercially viable, the price of the disposable photonic sensor chip should be as low as possible. The coupling of light from the source to the photonic sensor chip and back to the detectors requires a positioning accuracy of less than 1 micrometer, which is a tremendous challenge. In this research proposal, we want to investigate which of the six degrees of freedom (three translational and three rotational) are the most crucial when aligning photonic sensor chips with the external active components. Knowing these degrees of freedom and their respective range we can develop and test an automated alignment tool which can realize photonic sensor chip alignment reproducibly and fully autonomously. The consortium with expertise and contributions in the value chain of photonics interfacing, system and mechanical engineering will investigate a two-step solution. This solution comprises a passive pre-alignment step (a mechanical stop determines the position), followed by an active alignment step (an algorithm moves the source to the optimal position with respect to the chip). The results will be integrated into a demonstrator that performs an automated procedure that aligns a passive photonic chip with a terminal that contains the active components. The demonstrator is successful if adequate optical coupling of the passive photonic chip with the external active components is realized fully automatically, without the need of operator intervention.
