Back

Resource conflicts leading to moral distress: A longitudinal study among physicians in Norway

Miljeteig, I.; Forde, R.; Ro, K.; Baathe, F.; Bringedal, B.

2023-09-29 medical ethics
10.1101/2023.09.29.23295833 medRxiv
Show abstract

BackgroundThe scarcity of resources represents ethical challenges and involves moral distress for health professionals. There are no longitudinal studies of moral distress among representative samples of physicians. MethodSurveys of the Norwegian Physician Panel (NPP) compared the extent of moral distress in 2004 and 2021. Descriptive statistics and regression analysis were used in the study. ResultsResponse rates were 67% (1004/1499) in 2004 and 70% (1639/2316) in 2021. That patient care is deprived due to time constraints is the most severe dimension of moral distress among physicians, and it has increased comparing 2021 with 2004 (68.3% in 2004 to 75.1% in 2021 reported "somewhat" or "very morally distressing"). Moral distress also increased concerning patients who "cry the loudest" get better and faster treatment than others. Moral distress was reduced on statements about long waiting times, treatment not provided due to economic limitations, deprioritisation of older patients, and acting against ones conscience. Women reported higher moral distress than men in both years, and there were significant gender differences for six statements in 2021 and one in 2004. Though not consistently, the physicians age and workplace influenced the reported moral distress. ConclusionIn both years, moral distress among physicians related to scarcity of or unfair distribution of resources was high. Moral distress associated with resource scarcity and acting against ones conscience decreased, which might indicate improvements in the healthcare system. On the other hand, it might suggest that physicians have reduced their ideals or expectations or are morally fatigued.

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.