autoestima en el accountability para la toma de decisiones

El objetivo del presente estudio de caso es determinar si la autoestima es un factor que influye en el accountability para la toma de decisiones asertivas. Es un estudio no experimental, transversal, mixto, fenomenológico y descriptivo. Los instrumentos son: cuestionario para toma de decisiones, y accountability, prueba psicométrica y grupo focal. Los resultados muestran que los sujetos tienen las habilidades para la toma de decisiones, sin embargo su falta de seguridad y sus creencias limitantes no les permiten ser asertivos. La propuesta es incluir en el perfil de puesto el nivel de toma de decisiones y de seguridad para decidir e implementar capacitación en accountability. Abstract The objective of this study is to determine if self-esteem is a factor which influences accountability when assertive decisions are made. This is a non-experimental, cross-sectional, mixed, phenomenological, and descriptive study. The instruments are, a questionnaire for decision-making and accountability, a psychometric test, and a focus group. Results show that the subjects have the ability of decision-making, nevertheless, the lack of confidence and limiting beliefs do not allow them to be assertive. We propose to include in the job profile, the level of decision-making, and confidence in deciding, together, with the implementation of accountability training.

Information security education based on job profiles and the e-CF

PurposeThe information security field requires standardised education. This could be based on generic job profiles and a standard competence framework. The question is whether this is possible and feasible. To find out, the author did a case study: developing an information security master curriculum based on a generic PVIB job profile and the underlying competence framework e-CF.Design/methodology/approachThe research is a case study, using Design Science. Starting point is the specification of the learning goals for a cybersecurity master curriculum, using a generic PvIB job profile and the underlying competence framework e-CF. The curriculum has subsequently been developed, using backward design. Thereafter, the curriculum has been submitted for accreditation to test the successfulness of the approach.FindingsA generic job profile and a competence framework such as the e-CF support the development of standardised education. The generic PVIB job profile used works well. The e-CF can be useful, but requires modifications and the introduction of sub-competences. However, the main complaint concerning the e-CF is the use of examples instead of mandatory content.Originality/valueCompetence frameworks are available to formulate job descriptions, and are also suited for developing standardised education. Little research has been done on this. This case study shows that a competence framework is a useful tool for developing standardised education, although the e-CF may not be the most appropriate.

Big Data’s Disruptive Effect on Job Profiles: Management Accountants’ Case Study

The abundance of new innovative data sources creates opportunities and challenges for all professions and professionals working with information. One of these professionals is the management accountant (MA). Although their tasks have expanded over time and especially recently, MAs have not fully employed all the available internal and external data sources to describe, diagnose, visualize, predict and prescribe possible solutions that enable smart decisions with positive effects on businesses. Thus, the paper investigates the impact of Big Data, including Data Analytics, on MA’s job profile. Through a review of the most recent academic and professional publications, the paper contributes to the debate surrounding the redefinition of the role of MAs in organizations in a novel informational perspective of Abbott’s theory. The results could serve as a research agenda and incentive for further studies, as well as provide MAs with a guide on the topic of the enlargement of their role(s), respectively, the augmentation of their tasks and responsibilities regarding the analysis of Big Data. Furthermore, the research may provide both a rich and flexible framework to help practitioners in their analysis of potential risks, opportunities and challenges when handling Big Data, and a lens for professional accounting associations and bodies by helping them to prioritize the holding and seizing of jurisdictions as an imperative part of safety and security.

Personality Prediction using through CV Analysis

This Human Resource Management is obviously bolstered by and gave more open doors by the improvement of Job Characteristics Model (JCM) which thusly depends on the idea of present day occupation plan. Luckily, the advancement in present day data framework, computerized innovations, the general access of electronic innovation and web prompted the tendency of the worldwide Human Resource. The board improvement and make the framework more pertinent. Following the pattern, the proposed framework attempts to structure an arrangement to coordinate Job Characteristics Model into E-HR framework to scan for another model of proficient activity on Human Resource Management in the Internet Age. In this venture, we present a lot of strategies that makes the entirety enlistment process increasingly viable and productive. We have executed a framework that positions the competitors dependent on weight-age arrangement just as a bent test. Today there is a developing enthusiasm for the character attributes of an up-and-comer by the association to more readily look at and comprehend the competitor's reaction to comparable conditions. Along these lines, the framework directs a character expectation test to decide the character attributes of the applicant. At long last, it shows the consequences of the contender to the selection representative who assesses the top competitors and waitlists the applicant. This system can used in many business sectors that may require expert candidate. This system will reduce workload of the human resources. This system will help the human resource to select right candidate for desired job profile, which in turn provide expert workforce for the organization. Admin can easily shortlist a candidate based on their online test marks and select the appropriate candidate for particular job profile. This will enable a more effective way to short list submitted candidate CVs from a large number of applicants providing aconsistent and fair CV ranking policy, which can be legally justified

Factors affecting the level of professional development among nursing staff after the introduction of an electronic education monitoring system

Background: Constant improvement of the level of education, occupational independence, and the undertaking of scientific activity, including research and publications, contribute fundamentally to the development of the nursing profession. Ministerial directives for nurses to acquire and increase qualifications gave rise to the emergence of a new job profile in which self-discipline in the scope of scientific activity and personal development is essential. Aim of the study: The study aimed to assess the factors affecting the level of professional development among nurses after the introduction of the electronic education monitoring system (SMK), including variables hindering the process of increasing respondents’ qualifications. Material and methods: The research group consisted of 214 nurses who were both licensed to practice and practicing professionally. A diagnostic survey and questionnaire technique were used with the use of the author’s original questionnaire. Results: Nurses actively undertake postgraduate education, treating it more as a deep inner need and willingness to increase qualifications than a legal obligation. Among various forms of training, the biggest proportion took part in specialist courses – 24.9% (n=53), qualification courses – 23.8% (n=51), and specializations – 17.9% (n=38). Age (p=0.036) and length of professional service (p=0.001) were the most statistically relevant factors motivating the staff to undertake further educational activities. Conclusions: The factors significantly affecting the level of professional development in nurses are age, system of work, and issues arising while using the SMK. The introduction of training and clear instructions for using the system might contribute to the level of professional development among nurses.

Prevalence, Persistence, and Factors Associated with SARS-CoV-2 IgG Seropositivity in a Large Cohort of Healthcare Workers in a Tertiary Care University Hospital in Northern Italy

This observational study evaluated SARS-CoV-2 IgG seroprevalence and related clinical, demographic, and occupational factors among workers at the largest tertiary care University-Hospital of Northwestern Italy and the University of Turin after the first pandemic wave of March–April 2020. Overall, about 10,000 individuals were tested; seropositive subjects were retested after 5 months to evaluate antibodies waning. Among 8769 hospital workers, seroprevalence was 7.6%, without significant differences related to job profile; among 1185 University workers, 3.3%. Self-reporting of COVID-19 suspected symptoms was significantly associated with positivity (Odds Ratio (OR) 2.07, 95%CI: 1.76–2.44), although 27% of seropositive subjects reported no previous symptom. At multivariable analysis, contacts at work resulted in an increased risk of 69%, or 24% for working in a COVID ward; contacts in the household evidenced the highest risk, up to more than five-fold (OR 5.31, 95%CI: 4.12–6.85). Compared to never smokers, being active smokers was inversely associated with seroprevalence (OR 0.60, 95%CI: 0.48–0.76). After 5 months, 85% of previously positive subjects still tested positive. The frequency of SARS-COV-2 infection among Health Care Workers was comparable with that observed in surveys performed in Northern Italy and Europe after the first pandemic wave. This study confirms that infection frequently occurred as asymptomatic and underlines the importance of household exposure, seroprevalence (OR 0.60, 95%CI: 0.48–0.76).

Efficient Implementation using Multinomial Naive Bayes for Prediction of Fake Job Profile

The growing social media has increased the chances of fake job postings. To avoid fraudulent posts for job, an android application is designed for classification using machine learning. This paper proposes the implementation and working of machine learning based android application. For these various classifiers are used and results of these classifiers are compared for prediction of fake job profiles. Various single classifiers are used and based on the experimental results ,Multinomial Naive Bayes is the best classification to detect fake job over other classifiers.

Virtual Assistant for Prediction of Fake Job Profile Using Machine Learning

To avoid fraudulent post for job in the internet, an android application using machine learning based classification techniques is proposed in the paper. Different classifiers are used for checking fraudulent post in the web and the results of those classifiers are compared for identifying the best fake job scam detection model. It helps in detecting fake job posts from an enormous number of posts. It is an android application that is used to conduct an online conversation via text and speech by using natural language processing (NLP) and predicts the fake job offers using machine learning techniques. The data we used contains real and fake job post. We cleaned and pre-processed our data. After we implemented the classifiers, we trained and evaluated them for prediction of fake job offers.