Weaving rapport: doctors' strategies towards patients' noncompliance.

BACKGROUND: A successful therapeutic rapport between doctors and patients is built on effective doctor-patient communication. Noncompliance of patients which challenges their communication has been described in the research, yet the rapport strategies are not well discussed. METHODS: This qualitative study investigates the rapport strategies when doctors face noncompliance in consultations and its pragmatic effects achieved through the doctors' speeches. The 10-hour recordings come from the doctor-patient communication in the hospital setting. Thereafter, we analyze their conversation following the Spencer Oatey's rapport management model. RESULTS: Compliments and joking in the illocutionary domain, storytelling in the discourse domain, the doctors' participation in the participation domain and the choice of appropriate titles in the stylistic domain are identified and analyzed as the rapport-building strategies. CONCLUSION: The present study has offered insights into physicians' rapport-building strategies in the face of rapport-threatening behavior from patients. These strategies will help the doctors to deal with rapport-challenging behavior and boost overall patient wellness.

Modeling eye movement in dynamic interactive tasks for maximizing situation awareness based on Markov decision process.

For complex dynamic interactive tasks (such as aviating), operators need to continuously extract information from areas of interest (AOIs) through eye movement to maintain high level of situation awareness (SA), as failures of SA may cause task performance degradation, even system accident. Most of the current eye movement models focus on either static tasks (such as image viewing) or simple dynamic tasks (such as video watching), without considering SA. In this study, an eye movement model with the goal of maximizing SA is proposed based on Markov decision process (MDP), which is designed to describe the dynamic eye movement of experienced operators in dynamic interactive tasks. Two top-down factors, expectancy and value, are introduced into this model to represent the update probability and the importance of information in AOIs, respectively. In particular, the model regards sequence of eye fixations to different AOIs as sequential decisions to maximize the SA-related reward (value) in the context of uncertain information update (expectancy). Further, this model was validated with a flight simulation experiment. Results show that the predicted probabilities of fixation on and shift between AOIs are highly correlated ([Formula: see text] and [Formula: see text], respectively) with those of the experiment data.