美国州立监狱中的自杀事件:突出数据漏洞
Katherine LeMasters1,2, Michael F Behne2,3, Jennifer Lao2,3
1Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
PloS one
|May 31, 2023
概括
大多数美国州监狱系统缺乏及时的自杀数据,阻碍了解决监狱死亡的主要原因的努力. 需要可靠的报告来了解和减轻这些系统中的危害.
科学领域:
- 公共卫生 公共卫生
- 犯罪学 犯罪学
- 卫生政策 卫生政策
背景情况:
- 美国在全球范围内拥有最高的大规模监禁率,影响公共卫生.
- 自杀是州监狱系统的主要死亡原因,但缺乏实时的健康统计数据.
- 监狱政策显著影响自杀率,需要准确的数据来缓解.
研究的目的:
- 评估美国各州监狱系统自杀死亡率数据的可用性和报告情况.
- 记录2017-2021年监狱自杀数据的透明度和可访问性.
主要方法:
- 关于州监狱自杀的数据是从2017-2021年收集的.
- 信息来源是通过统计报告,新闻稿和信息自由法案请求.
- 各国的评估基于其报告的自杀数据的频率,细节性和可访问性.
主要成果:
- 只有16个州提供了频繁,细粒度和自由访问的自杀数据.
- 13个州提供了频繁的数据,但它缺乏细节性,不完整或无法自由获取.
- 13个州没有提供任何数据,8个州提供稀疏,不频繁或过时的信息.
结论:
- 大多数州未能遵守2000年"监狱死亡报告法"对自由提供数据的授权.
- 迫切需要有关监狱自杀,自杀企图和监禁条件的可靠实时数据.
- 改进数据收集和报告对于了解监狱系统的危害和倡导政策变革至关重要.
更多相关视频
04:36Setup and Execution of the Rapid Cycle Deliberate Practice Death Notification Curriculum
Published on: August 5, 2020
4.3K
09:55Bridging the Technology Divide in the COVID-19 Era: Using Virtual Outreach to Expose Middle and High School Students to Imaging Technology
Published on: September 28, 2022
1.7K
相关概念视频
The Stanford Prison Experiment
23.3K
The famous and controversial Stanford Prison Experiment, conducted by social psychologist Philip Zimbardo and his colleagues at Stanford University, demonstrated the power of social roles, social norms, and scripts.
23.3K
Healthcare Agencies II
728
There are various healthcare agencies in the United States—some of which are managed by religious institutions and others by different government branches.
Parish nursing is a growing specialty nursing profession that focuses on holistic healthcare, health promotion, and illness prevention. It blends professional nursing practice with a health ministry, focusing on health and healing within the context of a Christian community. Parish nurses serve as health educators, referral sources,...
Parish nursing is a growing specialty nursing profession that focuses on holistic healthcare, health promotion, and illness prevention. It blends professional nursing practice with a health ministry, focusing on health and healing within the context of a Christian community. Parish nurses serve as health educators, referral sources,...
728
Censoring Survival Data
154
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
154
Kaplan-Meier Approach
197
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
197
Psychosurgery
81
Psychosurgery, the surgical alteration or permanent removal of brain tissue to alleviate severe psychological conditions, stands as one of the most radical and controversial treatments in the history of mental health care. Its development and application have evolved significantly, marked by dramatic shifts in scientific understanding and ethical perspectives.
Historical Development of Psychosurgery
In the 1930s, Portuguese neurologist Antonio Egas Moniz introduced a surgical procedure designed...
Historical Development of Psychosurgery
In the 1930s, Portuguese neurologist Antonio Egas Moniz introduced a surgical procedure designed...
81
Assumptions of Survival Analysis
163
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
163
