The effect of guided group reflection on the ability and convenience of breaking bad news in pre-hospital emergency staff.

BACKGROUND: Bad news refers to any information that create negative changes in a person's understanding or expectations of in present and future. Breaking Bad News (BBN) is a stressful task that may have disturbing effects on the professional performance and general health of the medical staff. Pre-hospital emergency staff often needs to deliver bad news to the patient or his family. This study was conducted to determine the effect of guided group reflection training on the ability and comfort of BBN in pre-hospital emergency staff. METHODS: This quasi-experimental study was conducted on 95 staff of the pre-hospital emergency, in the test and the control groups. For the test group, a 4-hour training workshop on BBN was held, and then a group was formed in virtual space to discuss and exchange opinions about the scenarios of BBN and reflecting on it. Data collection tools were SPIKES Questionnaire and the Visual Analogue Mood Scale. The data were analyzed with SPSS V.18. RESULTS: The mean score of the ability to BBN after the intervention was 44.01 ± 6.21 in the test group and 31.40 ± 4.51 in the control group, and a significant difference was found using the independent t-test (P = 0.0001). Besides, the mean scores of the convenience of BBN in post-test was 5.52 ± 1.64 in the test group and 3.50 ± 1.28 in the control group using the independent t-test with a significant difference (P = 0.0001). CONCLUSION: According to the findings, training in guided group reflection improved the ability to BBN and its convenience in pre-hospital emergency staff. Therefore, it is suggested the use of this method in training for health care providers. Relating to BBN.

Applications of ultrasound techniques in tandem with non-destructive approaches for the quality evaluation of edible oils.

Edible oils include triglycerides that are extracted from oil seeds or fruits such as sunflowers, palms, olives, soys, rapeseeds, cocoa and many others. They are the elementary origins of unsaturated fats and vitamins especially vitamin 'E' in people's diets. Edible oils are at risk of intentional (such as inadequate storage conditions) and unintentional adulteration, so it is necessary to pay attention to their safety, health and fraud. Generally, this evaluation can be destructive or non-destructive. There are numerous methods to evaluate quality of edible oils which include sensory analysis, chemical analysis, chromatography, ultrasound, etc. The Ultrasonic approach is a non-destructive way and also fast, accurate, inexpensive, repeatable, portable and simple. Combination of ultrasound with other techniques such as electronic nose, electronic tongue, visible spectroscopic fingerprints, chemical descriptors, Raman spectroscopy, mid-infrared and machine vision, will improve quality evaluation and detection accuracy. This review summarizes the ultrasound idea and the latest knowledge of its application with other techniques on evaluation of edible oils.

The effect of data fusion on improving the accuracy of olive oil quality measurement.

Olive oil is one of the healthiest and most nutritious edible oils, and it has a great potential to be adulterated. In this research, fraud samples of olive oil were detected with six different classification models by fusion of two methods of E-nose and ultrasound. The samples were prepared in six categories of adulteration. The E-nose system included eight various sensors. 2 MHz probes were used in through transmission ultrasound system. Principal Component Analysis method was used to reduce features and six classification models were used for classification. Feature with the greatest influence in the classification was "percentage of ultrasonic amplitude loss." It was found that the ultrasound system's data had worked more effectively than the E-nose system. Results showed that the ANN method was recognized as the most effective classifier with the highest accuracy (95.51%). The accuracy of classification in all the classification models significantly increased with data fusion.

Fraud detection and quality assessment of olive oil using ultrasound.

Today, food safety is recognized as one of the most important human priorities, so effective and new policies have been implemented to improve and develop the position of effective laws in the food industry. Extra virgin olive oil (EVOO) has many amazing benefits for human body's health. Due to the nutritional value and high price of EVOO, there is a lot of cheating in it. The ultrasound approach has many advantages in the food studies, and it is fast and nondestructive for quality evaluation. In this study, to fraud detection of EVOO four ultrasonic properties of oil in five levels of adulteration (5%, 10%, 20%, 35%, and 50%) were extracted. The 2 MHz ultrasonic probes were used in the DOI 1,000 STARMANS diagnostic ultrasonic device in a "probe holding mechanism." The four extracted ultrasonic features include the following: "percentage of amplitude reduction, time of flight (TOF), the difference between the first and second maximum amplitudes of the domain (in the time-amplitude diagram), and the ratio of the first and second maximum of amplitude." Seven classification algorithms including "Naïve Bayes, support vector machine, gradient boosting classifier, K-nearest neighbors, artificial neural network, logistic regression, and AdaBoost" were used to classify the preprocessed data. Results showed that the Naïve Bayes algorithm with 90.2% provided the highest accuracy among the others, and the support vector machine and gradient boosting classifier with 88.2% were in the next ranks.