Chrome Extension
WeChat Mini Program
Use on ChatGLM

66 Changes in dignity and respect at the end of life: cross-sectional analysis of data from the VOICES bereavement survey (2011–2015)

BMJ(2019)

Cited 0|Views2
No score
Abstract
Background An aim of the National End of Life Care Strategy (2008) was to improve dignity and respect in end of life care (EoLC). The VOICES survey was commissioned in 2011 as the first national post-bereavement postal survey, a key component of which was to assess dignity and respect at the end of life. The aim of this study was to explore changes in dignity and respect perceived by bereaved relatives over the five-year period that the VOICES survey was commissioned. Methods Aggregate data from VOICES post-bereavement surveys (2011–2015) was obtained from the Office of National Statistics. Information about dignity and respect was extracted and dichotomised into satisfied (‘always’and ‘most of the time’) and unsatisfied (‘some of the time’and ‘never’). A chi-squared test for trend was used to analyse changes over time in dignity and respect, for each of seven categories of health care professionals. Results There were 1 07 206 responses to the VOICES surveys over 5 years (average response rate 44.4%). Improvements in perceived dignity and respect from 2011 to 2015 were found with respect to five categories of health care professional: GPs (0.7% improvement in satisfaction to 60.9%, p=0.016); hospital doctors (1.7% improvement to 85.0%, p=0.0036); hospital nurses (5.2% improvement to 80.6%, p Conclusions The data has demonstrated a general trend of improvement with regard to dignity and respect experienced from healthcare professionals. District/community nurses showed a decrease in dignity and respect however, this could be attributed to a regression to the mean or due to their high baseline, therefore this would not be cause for concern. The low satisfaction demonstrated with GPs should be investigated.
More
Translated text
Key words
voices bereavement survey,dignity,respect,cross-sectional
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined