Assisting upon entry: Helping type and approach as moderators of how role conflict affects newcomer resource drain.

We extend recent research on the costs and benefits of helping to help providers by asking whether and under what conditions newcomer help giving may amplify or mitigate the role-conflict-based resource drain such individuals may experience in the context of their initial socialization. Drawing from conservation of resources (COR) theory, we propose that whether providing assistance to peers enhances or weakens newcomer help providers' resilience to such conflict-based resource drain (i.e., exhaustion) depends on both the type of help given (instrumental vs. emotional) and the orientation (more vs. less empowering) that newcomers adopt when providing it. We test our propositions on the basis of time-lagged data collected from newly hired call center representatives at the end of their first and sixth months on the job. Results largely support our predictions, with instrumental assistance mitigating, and emotional assistance exacerbating, the role-conflict-based resource drain experienced by newcomer help providers. Moreover, these amplifying effects of emotional help provision on the conflict-exhaustion relationship are largely eliminated among those newcomer help providers reporting a more empowering approach to help provision. (PsycINFO Database Record

Detection of subclinical keratoconus using an automated decision tree classification.

PURPOSE: To develop a method for automatizing the detection of subclinical keratoconus based on a tree classification. DESIGN: Retrospective case-control study. METHODS: setting: University Hospital of Bordeaux. participants: A total of 372 eyes of 197 patients were enrolled: 177 normal eyes of 95 subjects, 47 eyes of 47 patients with forme fruste keratoconus, and 148 eyes of 102 patients with keratoconus. observation procedure: All eyes were imaged with a dual Scheimpflug analyzer. Fifty-five parameters derived from anterior and posterior corneal measurements were analyzed for each eye and a machine learning algorithm, the classification and regression tree, was used to classify the eyes into the 3 above-mentioned conditions. main outcome measures: The performance of the machine learning algorithm for classifying eye conditions was evaluated, and the curvature, elevation, pachymetric, and wavefront parameters were analyzed in each group and compared. RESULTS: The discriminating rules generated with the automated decision tree classifier allowed for discrimination between normal and keratoconus with 100% sensitivity and 99.5% specificity, and between normal and forme fruste keratoconus with 93.6% sensitivity and 97.2% specificity. The algorithm selected as the most discriminant variables parameters related to posterior surface asymmetry and thickness spatial distribution. CONCLUSION: The machine learning classifier showed very good performance for discriminating between normal corneas and forme fruste keratoconus and provided a tool that is closer to an automated medical reasoning. This might help in the surgical decision before refractive surgery by providing a good sensitivity in detecting ectasia-susceptible corneas.