Literature DB >> 32151521

Assessing patients´ preferences for breaking Bad News according to the SPIKES-Protocol: the MABBAN scale.

Pia von Blanckenburg1, Mareike Hofmann2, Winfried Rief3, Ulf Seifart4, Carola Seifart5.   

Abstract

OBJECTIVE: Quality of breaking bad news can seriously affect the course of disease. A frequently applied guideline is the SPIKES-Protocol that have been designed from the physician's perspective. Little is known about patients' preferences in breaking bad news. Our aim was to develop a questionnaire based on the SPIKES-protocol to detect patients´ preferences for breaking bad news communication.
METHODS: TheMarburg Breaking Bad News Scale (MABBAN) was developed and administered to 336 cancer patients. We used exploratory factor analysis. To examine potential relationships according to demographic and medical variables, regression analyses were conducted.
RESULTS: The novel questionnaire supported the six SPIKES-components of breaking bad news: Setting, Perception, Invitation, Knowledge, Emotions, and Strategy. Perception and Invitation clustered together to one subscale. Depending on clinical and demographic variables different components were rated as important.
CONCLUSION: Communication preferences in breaking bad news can be assessed using a SPIKES-based questionnaire. Physicians should improve the setting, share knowledge in all clarity, involve the patients in further planning, and consider demographical variables. PRACTICE IMPLICATIONS: Using SPIKES as a framework can optimize breaking bad news conversations but it seems important to emphasize the individual preferences beyond the six steps and tailor the communication process to the individual.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Assessment; Breaking bad news; Cancer; Oncology; Patient-Provider communication; Patients´Preferences; SPIKES

Mesh:

Year:  2020        PMID: 32151521     DOI: 10.1016/j.pec.2020.02.036

Source DB:  PubMed          Journal:  Patient Educ Couns        ISSN: 0738-3991


  8 in total

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Review 7.  A Review and Clinical Practice Guideline for Health Professionals Working With Indigenous and Culturally and Linguistically Diverse (CALD) Populations During COVID-19.

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8.  Machine Learning Approaches for Predicting Acute Respiratory Failure, Ventilator Dependence, and Mortality in Chronic Obstructive Pulmonary Disease.

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  8 in total

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