Effects of medical communication on patient's emotional well-being, quality of care and trust in healthcare professionals: a multi-modal assessment study in oncology settings
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Francesca Alby | Componenti strutturati del gruppo di ricerca |
Ilaria Bufalari | Componenti strutturati del gruppo di ricerca |
Because effective doctor-patient communication has many benefits in cancer care, we primarily focus on the social interaction between the oncologist and the patient, that we expect to include the process through which communication can change the emotional state of the patient before and after the visit, and pave the way for patient satisfaction and trust in the physician. The study uses a multimodal approach. Doctor-patient interactions will be video-recorded to extract five behavioral indicators. Communicative behaviors of the physician, communicative behaviors the patient, misalignment in co-orientation and affiliation, expressions of patient confidence in the doctor, and linguistic markers of clinical uncertainty, will be used to explain positive and negative affective reactions after the visit controlling for the baseline emotional state at pretest, assessed using self-report and physiological data. The five categories will also be used to predict the perceived communication skills of the doctor and the patient's trust. Last, age, gender, ethnicity, and other trait-like dispositions will be used in data analysis to test a third research question, that is how individual and cultural differences that might bias doctor-patient communication. The research comprises two phases. Phase 1 will develop and pretest a reliable coding system for doctor-patient interactions during the visit and will involve about one-third of the total sample (approximately 20 patients). Phase 2 will verify the substantive hypotheses outlined above and will require about two-thirds of the sample (about 40 patients). Preliminary analyses will establish the reliability of the coding system and preprocess ECG data to obtain indices of emotional arousal. Substantive hypotheses will be tested using Partial Least Squares Structural Equation Modeling, a non-parametric approach suited for nonnormal or highly skewed data as well as with small samples.