LONDON (Reuters), May 22 - One in four National Health Service staff have been bullied or harassed by patients and their families in the last 12 months and the problem is growing, according to a report on Friday.
The British Medical Association report said bullying appeared to be a serious issue in the NHS, with 15 percent of respondents also reporting suffering at the hands of their colleagues.
"The cycle of bullying in medicine has to stop," said Sam Everington, deputy chairman of the BMA which is calling for a "zero tolerance" culture to be implemented.
Junior doctors and medical students reported they often bore the brunt of bullying as part of an "initiation rite" into the profession. A quarter said senior colleagues had bullied them while 16% said they had been picked on by nurses.
"It's not good enough for a senior doctor to think that he or she had a hard time and was humiliated as a medical student so it's justified for them to dole out the same treatment," Everington said.
He added the problem went across the board, although doctors from overseas were particularly vulnerable.
"Consultants can be bullied by peers and by managers. The highly pressurized target ethos in the health service only adds to the survival of the fittest culture where bullying is often seen as a way of motivating staff."
Belittling and undermining work, withholding information, freezing out and removing areas of responsibility without consultation and setting impossible deadlines were cited as the most common forms of bullying or harassment.
An under-reporting of incidents might also mask the extent of bullying within the NHS, Britain's largest employer, the report said.
It added workplace bullying affected up to half the total U.K. workforce, costing employers 80 million pounds in lost working days and 2 million pounds in lost revenue.
Last Updated: 2006-05-19 12:44:28 -0400 (Reuters Health)
Related Reading
Third of UK doctors worried by patient violence, October 16, 2003
Copyright © 2006 Reuters Limited. All rights reserved. Republication or redistribution of Reuters content, including by framing or similar means, is expressly prohibited without the prior written consent of Reuters. Reuters shall not be liable for any errors or delays in the content, or for any actions taken in reliance thereon. Reuters and the Reuters sphere logo are registered trademarks and trademarks of the Reuters group of companies around the world.
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
