Background and Objectives:As key members of healthcare teams, nurses need to establish communications with other healthcare providers, patients, and family members. Communication has three main types, namely passive, aggressive, and assertive. The most effective type of communication is the assertive type. Assertiveness is defined as the abilities to say no, express desires and negative/positive feelings, and start, continue, and finish a conversation. Assertiveness has many benefits for both nurses and patients. Yet, there are limited studies on assertiveness among Iranian hospital nurses. This study aimed to evaluate assertive behaviors among nurses in Qaen, an eastern city in Iran. Methods: This cross-sectional descriptive-analytical study was conducted on all 160 nurses, auxiliary nurses, and anesthesia and operating room technicians who were working at Shoahday-e Qaen hospital, Qaen, Iran, in summer 2017. A demographic questionnaire and the valid and reliable Gambrill-Richey assertion inventory were employed for data collection. The SPSS for Windows program (v. 21.0) was used to analyze the data by running the Chi-square test. Results: In total, 141 participants completely filled out and returned their questionnaires. They were mostly female (67.4%) and married (82.3%). The mean of their age was 31.49 ± 7.3 with a range of 18 -52. Only 21.3% of them were assertive, while the remaining 78.7% were either unassertive (31.2%), anxious performer (32.6%), or indifferent (14.9%).
Conclusions:The hospital nursing staff has limited assertiveness. Therefore, educational programs are needed to promote their communication skills and assertiveness. Improvement of nurses' awareness and knowledge of communication skills and assertiveness can improve nurse-patient relationships, care quality, and patient outcomes.
High resolution range profile (HRRP) is a widely noted tool in radar target recognition. However, its high sensitivity to the target's aspect angle makes it necessary to seek solutions for this problem. One alternative is to assume consecutive samples of HRRP identically and independently distributed in small frames of aspect angles, an assumption which is not true in reality. Based on this simplifying assumption, some models, such as the hidden Markov model, have been developed to characterise the sequential information contained in multi‐aspect radar echoes. As a result, these models consider only the short dependency between consecutive samples. Considering such issues, in this study, the authors propose an alternative polynomial segment model. In addition, using a Markov chain Monte–Carlo based Gibbs sampler as an iterative approach to estimate the parameters of the segment model, the authors will show that the results are quite satisfactory.
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