Anxiety in first year medical students taking gross anatomy.

To study anxiety levels in first-year medical students taking gross anatomy. Thirty medical students per year, for 2 years, completed the Beck Anxiety Inventory (BAI) 10 times during a 13-week gross anatomy course. In addition, behavioral observations were made by a psychiatrist during gross anatomy for demonstrations of assertive, destructive, neutral, or passive behavior. Additional qualitative outcome measures were group exit interviews with the faculty and students. The mean BAI for all 60 students per year, for 2 years, was 2.19 ± 3.76, 93% of the scores indicated minimal anxiety, and 89% of BAI values were less than five which confirmed a minimal level of anxiety. The low level of reported BAI contrasted sharply with verbal reports by the same students and face-to-face exit interviews with the psychiatrist. Symptoms of stress and anxiety emerged as a result of these conversations. The high levels of subjective stress and anxiety revealed by the interviews were unknown to the gross anatomy faculty. The low scores of students on the BAI's stand in sharp contrast to the BAI's reported for medical students in other published reports. Although it is possible that our students were truthfully devoid of anxiety, it is more likely that our students were denying even minimal anxiety levels. There have been reports that medical students feel that admitting stress, depression, or anxiety put their competitiveness for a residency at risk. We conclude that students may be in frank denial of experiencing anxiety and, if so, this behavior is not conducive to good mental health.

Imputation of confidential data sets with spatial locations using disease mapping models.

Data that include fine geographic information, such as census tract or street block identifiers, can be difficult to release as public use files. Fine geography provides information that ill-intentioned data users can use to identify individuals. We propose to release data with simulated geographies, so as to enable spatial analyses while reducing disclosure risks. We fit disease mapping models that predict areal-level counts from attributes in the file and sample new locations based on the estimated models. We illustrate this approach using data on causes of death in North Carolina, including evaluations of the disclosure risks and analytic validity that can result from releasing synthetic geographies.