Intention to comply with post-exposure management among nurses exposed to blood and body fluids in Taiwan: application of the theory of planned behaviour.

Nurses are at significant risk from occupationally acquired bloodborne virus infections following a needlestick and sharps injury. This study aimed to apply the theory of planned behaviour (TPB) to predict nurses' intention to comply with occupational post-exposure management. A cross-sectional survey was applied to select registered nurses who worked in human immunodeficiency virus (HIV)-designated hospitals. An anonymous, self-administered questionnaire based on the TPB was distributed to 1630 nurses and 1134 (69.5%) questionnaires were returned. From these, a total of 802 nurses (71%) reported blood and body fluid exposure incidents during 2003-2005 and this group was used for analysis. Only 44.6% of the 121 exposed nurses who were prescribed post-exposure prophylaxis (PEP) by infectious disease doctors returned to the clinic for interim monitoring, and only 56.6% of exposed nurses confirmed their final serology status. Structural equation modelling was used to test the TPB indicating perceived behavioural control (the perception of the difficulty or ease of PEP management, ß=0.58), subjective norm (the perception of social pressure to adhere to PEP, ß=0.15), and attitudes (ß=0.12) were significant direct effects on nurses' intention to comply with post-exposure management. The hypothesised model test indicated that the model fitted with the expected relationships and directions of theoretical constructs [χ(2) (14, N=802)=23.14, P=0.057, GFI=0.987, RMSEA=0.039]. The TPB model constructs accounted for 54% of the variance in nurses' intention to comply with post-exposure management. The TPB is an appropriate model for predicting nurses' intention to comply with post-exposure management. Healthcare facilities should have policies to decrease the inconvenience of follow-up to encourage nurses to comply with post-exposure management.

An approach to artifact identification: application to heart period data.

A rational strategy for the automated detection of artifacts in heart period data is outlined and evaluated. The specific implementation of this approach for heart period data is based on the distribution characteristics of successive heart period differences. Because beat-to-beat differences generated by artifacts are large, relative to normal heart period variability, extreme differences between successive heart periods serve to identify potential artifacts. Critical to this approach are: 1) the derivation of the artifact criterion from the distribution of beat differences of the individual subject, and 2) the use of percentile-based distribution indexes, which are less sensitive to corruption by the presence of artifactual values than are least-squares estimates. The artifact algorithms were able to effectively identify artifactual beats embedded in heart period records, flagging each of the 1494 simulated and actual artifacts in data sets derived from both humans and chimpanzees. At the same time, the artifact algorithms yielded a false alarm rate of less than 0.3%. Although the present implementation was restricted to heart period data, the outlined approach to artifact detection may also be applicable to other biological signals.