Quantifying culture gaps between physicians and managers in Dutch hospitals: a survey

Background: The demands in hospitals for safety and quality, combined with limitations in financing health care require effective cooperation between physicians and managers. The complex relationship between both groups has been described in literature. We aim to add a perspective to literature, by developing a questionnaire which provides an opportunity to quantitatively report and elaborate on the size and content of differences between physicians and managers. Insight gained from use of the questionnaire might enable us to reflect on these differences and could provide practical tools to improve cooperation between physicians and managers, with an aim to enhance hospital performance.\ud \ud Methods: The CG-Questionnaire was developed by adjusting, pre-testing, and shortening Kralewski's questionnaire, and appeared suitable to measure culture gaps. It was shortened by exploratory factor analysis, using principal-axis factoring extraction with Varimax rotation. The CG-Questionnaire was sent to all physicians and managers within 37 Dutch general hospitals. ANOVA and paired sample T-tests were used to determine significant differences between perceptions of daily work practices based in both professional cultures; culture gaps. The size and content of culture gaps were determined with descriptive statistics.\ud \ud Results: The total response (27%) consisted of 929 physicians and 310 managers. The Cronbachs alpha's were 0.70 - 0.79. Statistical analyses showed many differences; culture gaps were found in the present situation; they were even larger in the preferred situation. Differences between both groups can be classified into three categories: (1) culture gaps in the present situation and not in the preferred, (2) culture gaps in the preferred situation and not in the present, and (3) culture gaps in both situations.\ud \ud Conclusions: With data from the CG-Questionnaire it is now possible to measure the size and content of culture gaps between physicians and managers in hospitals. Results gained with the CG-Questionnaire enables hospitals to reflect on these differences. Combining the results, we distinguished three categories of increasing complexity. We linked these three categories to three methods from intergroup literature (enhanced information, contact and ultimately meta cognition) which could help to improve the cooperation between physicians and managers

ROC analysis of lineups obscures information that is critical for both theoretical understanding and applied purposes

Our previous article (Wells et al., 2015a. Journal of Applied Research in Memory and Cognition) showed how ROC analysis of lineups does not measure underlying discriminability or control for response bias. Wixted and Mickes (2015. Journal of Applied Research in Memory and Cognition) concede these points. Hence, in this article we focus more on how forcing the 3. ×. 2 lineup into the 2. ×. 2 structure required for ROC analysis obscures important underlying phenomena of theoretical value. Moreover, ROC analysis fails to account for the unique diagnostic properties of exonerating eyewitness behaviors (filler identifications and rejections). We describe how an examination of the full 3. ×. 2 structure helps reveal the critical underlying phenomena that ROC analysis hides. We also show how a Bayesian approach yields a family of diagnosticity functions that exposes the unique diagnosticity of all three eyewitness behaviors (suspect identifications, filler identifications, and rejections). Moreover, we show how Bayesian methods can examine diagnosticity as a function of witness confidence for all three eyewitness behaviors, which gives it a significant applied advantage over ROC analysis

ROC analysis of lineups does not measure underlying discriminability and has limited value

Some researchers have been arguing that eyewitness identification data from lineups should be analyzed using Receiver Operating Characteristic (ROC) analysis because it purportedly measures underlying discriminability. But ROC analysis, which was designed for 2. ×. 2 tasks, does not fit the 3. ×. 2 structure of lineups. Accordingly, ROC proponents force lineup data into a 2. ×. 2 structure by treating false-positive identifications of lineup fillers as though they were rejections. Using data from lineups versus showups, we illustrate how this approach misfires as a measure of underlying discriminability. Moreover, treating false-positive identifications of fillers as if they were rejections hides one of the most important phenomena in eyewitness lineups, namely filler siphoning. Filler siphoning reduces the risk of mistaken identification by drawing false-positive identifications away from the innocent suspect and onto lineup fillers. We show that ROC analysis confuses filler siphoning with an improvement in underlying discriminability, thereby fostering misleading theoretical conclusions about how lineups work

Deviation from Perfect Performance Measures the Diagnostic Utility of Eyewitness Lineups but Partial Area Under the ROC Curve Does Not

When one lineup identification procedure leads to both fewer innocent–suspect identifications and fewer culprit identifications than does some other lineup procedure, it is difficult to determine whether the procedures differ in diagnostic accuracy. In an influential article, Wixted and Mickes (2012) argued that measures of probative value do not inform diagnostic accuracy in these situations but that the partial area under the receiver operator characteristic curve (pAUC) does. In more recent research, we have found that pAUC does not necessarily indicate which of two lineup procedures has higher expected utility. When two lineup procedures produce different innocent-suspect identification rates, it leads to differential truncation of the ROC curves. As a result, diagnostic utility as measured by the pAUC is confounded with witness confidence level. We introduce a novel receiver operator characteristic measure, deviation from perfect performance (DPP), that unconfounds diagnostic utility and witness confidence level and consistently indicates which of two lineup procedures has higher expected utility. Our findings suggest that eyewitness scientists should abandon pAUC as a measure of diagnostic accuracy and embrace deviation from perfect performance