Among Nursing and Psychology professionals, the job satisfaction of those in Adult Inpatient Units is analyzed, with a new scale to measure nursing workloads validated. Objective: The objective of this study was to relate nursing workloads to professional job satisfaction. Methods: This is an observational, analytical, descriptive, concurrent and quantitative study, which used the Overall Job Satisfaction scale and subscales therein, to identify global satisfaction—intrinsic or related to motivational factors and extrinsic or associated with hygienic factors—in nursing professionals (n = 104) from eight Inpatient Units of Internal Medicine and Surgery, in four hospital centers, to describe job satisfaction in the professionals studied and to find statistically significant associations between job satisfaction and workload (measured with the scale MIDENF^®) in the inpatient units where they work. Results: There were higher levels of satisfaction in the variables “relationship with immediate boss” and “relationship with fellow workers”, and lower levels in “relationship with senior management” and “organizational system of the unit”. In the inferential analysis, the scores were 75.63 for overall satisfaction, 35.28 for intrinsic satisfaction, and 40.36 for extrinsic satisfaction. Conclusions: There is a close relationship between workload and job satisfaction, showing more dissatisfaction regarding organizational aspects and professional recognition.

The previous step to be able to carry out a complete and reliable inferential statistical analysis of the data collected in a research is the execution of a correct and exhaustive descriptive statistical analysis. At this stage, we understand the main characteristics of our sample, which allows us to address the complete description of the sample in our research work and, in addition, offers us a first approximation of the variables in which there could be differences depending on others. It is only an approximation. Subsequently, a statistical inferential test will have to say whether statistically speaking they exist or not. This paper presents the main descriptive statistics and their corresponding graphs depending on the type of variable, as well as their calculation and representation using the free statistical software jamovi, a powerful free tool that does not require advanced programming knowledge. Resumen El paso previo para poder realizar un análisis estadístico inferencial completo y fiable de los datos recolectados en una investigación es la ejecución de un correcto y exhaustivo análisis estadístico descriptivo. En esta etapa, comprendemos las principales características de nuestra muestra, lo que nos permite abordar la descripción completa de la misma en nuestro trabajo de investigación y, además, nos ofrece una primera aproximación sobre las variables en las que podría haber diferencias en función de otras. Solo es una aproximación. Posteriormente, una prueba estadística inferencial tendrá que decir si, estadísticamente hablando, existen o no. En este trabajo se presentan los principales estadísticos descriptivos y sus correspondientes gráficos en función del tipo de variable, así como su cálculo y representación a través del software estadístico libre jamovi, como potente herramienta gratuita que no requiere de un conocimiento previo avanzado en programación.

Background: Very young children, and those with disabilities and extensive oral pathology, who cannot be treated in the dental chair, require deep sedation or general anesthesia for dental treatment. Objective: The aim of this study is to describe and compare the oral health status in healthy and SHCN children and the treatments performed under deep sedation on an outpatient basis with a minimal intervention approach, and their impact on quality of life. Methods: A retrospective study between 2006 and 2018 was made. A total of 230 medical records of healthy and SHCN children were included. The data extracted were age, sex, systemic health status, reason for sedation, oral health status before sedation, treatments administered during sedation, and follow-up. The quality of life after deep sedation of 85 children was studied through parental questionnaires. Descriptive and inferential analyses were made. Results: Of the 230 children, 47.4% were healthy and 52.6% were SHCN. The median age was 7.10 ± 3.40 years (5.04 ± 2.42 in healthy children and 8.95 ± 3.09 in SHCN children). The main reason for sedation was poor handling in the dental chair (99.5%). The most frequent pathologies were caries (90.9%) and pulp pathology (67.8%). Healthy children had more teeth affected by decay and with pulp involvement. Patients aged < 6 years received more pulpectomies and pulpotomies. After treatment, parents stated that children were more rested and less irascible, ate better, increased in weight, and had improved dental aesthetics. Conclusions: Differences in treatments carried out did not depend on the general health status or the failure rate but on age, with more pulp treatments in healthy children who were younger, and more extractions near to the age of physiological turnover in children with SHCN who were older. Intervention under deep sedation with a minimally invasive treatments approach met the expectations of parents and guardians, as it improved the children’s quality of life.

En el presente trabajo se pretende conocer la percepción que tuvo el alumnado del Grado en Educación Primaria de una universidad estatal sobre la implementación de una serie de actividades prácticas de forma virtual durante el confinamiento establecido por el coronavirus COVID-19, y analizar la adquisición de las competencias en su formación matemático-estadística a través de su realización. Con un diseño cuantitativo descriptivo y la aplicación de una encuesta tipo escala Likert, los resultados reflejan que el alumnado participante consideró que estas actividades ayudan a la consecución de los objetivos del contenido didáctico y de las competencias asociadas, y que las aplicarían en un futuro como profesionales docentes.

We conducted validation of a scale to measure nursing workloads, previously designed using NIC interventions within the four nursing functions (patient care, teaching, management, and research). Methods: This is an analytical, descriptive, prospective, and observational study using qualitative methodology (focus groups and in-depth interviews) with a quantitative and qualitative section (committee of experts and real application of the scale through a validation pilot and with multicentric application, including hospitalization units of internal medicine and surgery of four hospitals). Qualitative analysis was performed with Atlas.ti8 and quantitative analysis with R. Results: Qualitatively, all the participants agreed on the need to measure workloads in all nursing functions with standardized terminology. The expert committee found greater relevance (91.67%) in “prevention” and “health education” as well as consistency with the construct and adequate wording in 99% of the selected items. In the pilot test and multicenter application, the nurses spent more time on the caring dimension, in the morning shift, and on the items “self-care”, “medication”, “health education”, “care of invasive procedures”, “wounds care”, “comfort”, and “fluid therapy”. Cronbach’s alpha 0.727, composite reliability 0.685, AVE 0.099, and omega coefficient 0.704 were all acceptable. Construct validity: KMO 0.5 and Bartlett’s test were significant. Conclusions: The scale can be considered valid to measure nursing workloads, both qualitatively in obtaining the consensus of experts and health personnel and quantitatively, with acceptable reliability and validity superior to other similar scales.

El análisis estadístico descriptivo es el primer paso que cualquier persona que pretenda realizar una investigación cuantitativa debe dar cuando se dispone a realizar el análisis de los datos recogidos durante el trabajo de campo de su investigación. Es fundamental conocer de una forma descriptiva las características de la población participante en el estudio y los resultados iniciales obtenidos, como paso previo a la realización de análisis inferenciales o al ajuste de modelos estadísticos que expliquen de forma más profunda los datos obtenidos. Este trabajo presenta una introducción a las diferentes variables que nos podemos encontrar y a la implementación de su análisis descriptivo mediante el software estadístico libre jamovi. Se muestra, por tanto, una herramienta sin coste alguno que ofrece resultados potentes sin necesidad de conocer un lenguaje de programación estadístico.