Preparing Advanced Clinicians and Practitioners: A Model for Mentorship in Occupational Therapy Practice

Mentoring is essential at all stages of a professional career. However, little has been written about the effectiveness of programs for practicing clinicians. This study was designed to address the need for evidence about the effectiveness of formal mentorship programs by describing the impact of the STAR mentorship program on a group of clinicians specializing in sensory integration and processing challenges. This study utilized an exploratory, retrospective, survey research design. Course evaluations were examined from 240 subjects following participation in a one-week, small group mentorship training program. Qualitative methods were adapted for use in this study. Sixteen codes, with operational definitions, were developed to analyze the surveys. Ninety-six percent indicated that the program met or exceeded their expectations; only 12.5% had a negative comment. Impact on psychosocial function was reflected by 22% of the participants. Comments related to impact on career function were indicated by 45% of the participants. Ninety-four percent provided positive comments about the program structure, and 74% responded with positive comments regarding content of the program. Positive outcomes were associated with this one mentorship program, suggesting a need for more in-person, structured mentored learning experiences. Mentorship is recommended as a method to address the growing need within the profession to support career development, build knowledge, skill and attitudes, and aspirations/commitment as well as enhance professionalism/professional development.

The Power of Words: Appearance Comments from One’s Partner Can Affect Men’s Body Image and Women’s Couple Relationship

Background: The purpose of the present study was to examine, through an experimental vignette design, the effects of appearance-related comments from one’s partner on body image and the perceived quality of one’s relationship. Body image was considered both in negative (body dissatisfaction) and positive (body compassion) terms. Methods: Appearance-related commentary from one’s partner was manipulated through a vignette describing the purchase of a swimsuit. The participants (n = 211) were women and men who were randomly assigned to one of the three experimental conditions (positive comment, negative comment, no comment). Results: A series of ANOVAs showed different findings for women and men. Being criticized for body weight and shape caused an increase in body dissatisfaction and a decrease in body compassion in men but not in women. Regarding couple satisfaction, women who imagined receiving a compliment about their body perceived being more accepted by their partner and were less afraid of being abandoned or rejected. Conclusions: Our findings highlight the importance of appearance-based comments from one’s partner on men’s body image and on women’s perception of their couple relationship. Therefore, appearance comments might be addressed by interventions aimed at enhancing positive body image, reducing body dissatisfaction, and fostering couple relationships, carefully considering sex differences.

Multitasking and correction of false information

Research shows that high levels of media multitasking (either situationally induced or chronic) may be associated with a decreased cognitive function. Since cognitive capacity is required for efficient correction of one’s judgment after learning that the judgement base is no longer valid, we expected that high levels of media multitasking would decrease one’s ability to adequately update their beliefs. We ran two studies in which participants were asked to form an impression of a target person based on their online profile from a professional networking site. The profile contained either neutral information (control condition) or negative comment from a former supervisor which was later debunked (false information conditions). We additionally manipulated media multitasking demands (in Study 1) or measured participants’ frequency of media multitasking (Study 2) and tested whether the level of media multitasking is related to the degree to which the initial attitudes were adjusted after learning that the negative comment was false. We found a significant but rather small effect of manipulation in Study 1 indicating that participants in both multitasking conditions had more negative attitudes after correction compared to the baseline, but not to the mono-tasking condition. Crucially, media multitasking demands did not impact attitude adjustment. Results of Study 2 showed that the relationship between media multitasking frequency measured with a scale and attitude adjustment were non-significant. Overall, the current findings suggest that media multitasking, experimentally manipulated or chronic, plays a negligible role in correction after misinformation.

Understanding intent, behavior and emotion in user comments

This study tests the process of writing a user comment on a news website: it follows the user's exposure to an online article, exposure to others’ comments, decision to write their own comment (or not), and the social behavior of writing itself, as well as the characteristics of the writing. We aim to examine whether exposure to positive and negative user comments on an online journalistic article affects the user's intention to send a comment and, more importantly, the sentiment of the comment posted. We also test whether a user's opinion or their perception of majority support of their opinion have an impact on either their intention to post a comment or their comments’ sentiment. Results show that exposure to comments, whether positive or negative, almost doubles the intentions of posting a comment. Exposure to negative comments dramatically increases the probability of writing a negative comment, while exposure to positive comments has a moderating effect. In line with the spiral of silence theory, self-perceived support for one’s opinion by the majority significantly contributes to the prediction of comment posting, even after controlling for the effect of personal opinion.

Fruitfly based Optimization for Forgery Face Detection Analysis

Computerized imaging is huge development in ongoing decades, and these pictures is being utilized in developing number of uses. These days a few virtual products are accessible that are utilized to control picture so the picture resembles the first picture. Pictures are utilized as confirmed evidence for any wrongdoing and in the event that these pictures are not veritable, at that point it will make a doubt. The accessible minimal effort equipment and programming apparatuses makes it simple to control the first pictures with no conspicuous follows. Picture falsifications are developing at a disturbing rate in different fields and has offered negative comment in tolerating the respectability and realness of the first pictures. Destroying in an advanced picture has become a difficult assignment. The reliability of the pictures has been an inquiry because of the huge development in picture control devices. The AI and enhancement calculations are utilized to get viable outcomes. In our project, forgery detection is based on Support Vector Neural Network. The pictures are gathered and the face is recognized utilizing robust skin colored based algorithm and these pictures are exposed to feature extraction, which is prepared utilizing fruit fly optimization algorithm to group the features to identify the manipulation. The metrics, accuracy, sensitivity and specificity of the image is obtained as the result.

Mining Negative Comment Data of Microblog Based on Merge-AP

A new depiction method based on the merge-AP algorithm is proposed to effectively improve the mining accuracy of negative comment data on microblog. In this method, we first employ the AP algorithm to analyze negative comment data on microblog and calculate the similarity value and the similarity matrix of data points by Euclidean distance. Then, we introduce the distance-based merge process to solve the problem of poor clustering effect of the AP algorithm for datasets with the complex clustering structure. Finally, we compare and analyze the performance of K-means, AP, and merge-AP algorithms by collecting the actual microblog data for algorithm evaluation. The results show that the merge-AP algorithm has good adaptability.

Pesan-Pesan Dakwah Akun Instagram @sahabat_islami Dalam Meningkatkan Kesadaran Keagamaan (Studi Pada Mahasiswa Komunikasi dan Penyiaran Islam (KPI) Fakultas Dakwah dan Komunikasi UIN Ar-Raniry)

This article examines the message of preaching in the Instagram media @sahabat_islami, because along with the development of the era, the netizens use Instagram media as a media in reviewing about the Islamic, with the media, the messages will be delivered quickly to the followers. This study uses a qualitative research type with a descriptive approach. It is used to examine the design as a whole, while the approach is a descriptive approach with the goal of being able to describe comprehensively the research results obtained in the field. The results of the discussion say that the messages of Da'wah through the media istagram @sahabat_islami contain the message of Aqidah, sharia message, and Akhlak. Netizen comments on the post are positive and negative. Positive comments are helpful for gaining insight into Keislaman, a negative comment saying that the account is indicated by the business. The message of preaching on the Instagram account @sahabat_islami in raising religious awareness is, since becoming an Instagram follower they have an impact in the better direction, and often practice about the knowledge gained through the postings on the account.

Sentiment Analysis of Product Reviews as A Customer Recommendation Using the Naive Bayes Classifier Algorithm

In an e-commerce Shopee, the process of selling and buying continues to run every day, and the comments given by consumers will increase more and more. Comments given by consumers will be the reference/review of a product that has been purchased by consumers. Consumers freely provide a review containing positive comments and negative comments in the Comments field listed on the Shopee e-commerce website. With the above problems, researchers will do a research with the method of sentiment analysis to distinguish classes in product review comments that include positive comment class or negative comment class using a combination of K-means and naive Bayes classifier. K-means used to determine the grouping of classes; naive Bayes classifier used to get the value of accuracy. The results obtained based on clustering K-means include getting 116 negative comments on product reviews and 37 negative comments product reviews. Accuracy results obtained from product review comment data of 77.12%. Thus, the accuracy value using K-means and naive Bayes classifier without manual data get a higher accuracy value is compared using K-means, Naive Bayes classifier, and manual data get results lower accuracy of 56.86%. From the results above the most comments is a negative comment of 116 data review comments product, from the results of the study can be concluded that one of the products of Spatuafa named high heels women know the Ribbon Ikat FX18 the condition of the product is not good enough due to the high negative comments compared to positive comments