María Rubio-Aparicio

El diagrama de flujo constituye una herramienta fundamental para ilustrar de manera clara y transparente todo el proceso de búsqueda y selección de los estudios incluidos durante la realización de una revisión sistemática, con o sin meta-análisis. Además, facilita la evaluación y garantiza la replicabilidad del procedimiento. La Declaración PRISMA 2020 recomienda explícitamente su uso en el ítem 16, y otras organizaciones, como la APA, promueven su inclusión entre los estándares de calidad. El diagrama debe representar cuatro fases clave: identificación, cribado, elegibilidad e inclusión, detallando los registros identificados, las exclusiones y las razones generales de exclusión. Se aconseja elaborarlo desde el inicio del proceso para evitar inconsistencias posteriores. PRISMA ofrece plantillas y herramientas interactivas para apoyar a los/as investigadores/as en su aplicación y adaptación. Aunque su uso ha aumentado con el tiempo, sigue siendo inferior a lo deseable, a pesar de estar asociado con mejoras significativas en la calidad metodológica. Por ello, su preparación cuidadosa conforme a los estándares establecidos no debe considerarse como un mero requisito formal, sino como una práctica que refuerza la rigurosidad y la credibilidad de la investigación. Abstract The flow diagram is an essential tool for clearly and transparently illustrating the entire process of searching for and selecting studies included in a systematic review, with or without meta-analysis. It also facilitates evaluation and ensures procedural replicability. The PRISMA 2020 Statement explicitly recommends its use in item 16, and other organizations, such as the APA, advocate its inclusion among quality standards. The diagram should depict four key phases: identification, screening, eligibility, and inclusion, detailing identified records, exclusions, and general reasons for exclusion. Constructing the diagram from the outset is advised to prevent inconsistencies. PRISMA provides interactive templates and tools to support researchers in its application and adaptation. Although its use has grown over time, it remains below optimal levels, despite being linked to significant improvements in methodological quality. Therefore, careful preparation in line with established standards should be viewed not as a mere formal requirement, but as a practice that strengthens the rigor and credibility of research.

Las revisiones sistemáticas (RS) y los meta-análisis (MA) constituyen un tipo de investigación fundamental para una adecuada acumulación del conocimiento en cualquier ciencia empírica. Si bien los MA permiten alcanzar conclusiones más sólidas sobre la pregunta de interés, no siempre se dan las condiciones adecuadas para su aplicación. En su defecto, pueden aplicarse RS. Se presentan las diferencias y similitudes entre las RS y los MA. Se presentan las fases en las que se lleva a cabo una RS sin MA: planteamiento de la pregunta, definición de los criterios de selección de los estudios, búsqueda de los estudios, extracción de la información, valoración del riesgo de sesgo, medida del resultado de los estudios, métodos de síntesis y redacción del informe. Se ofrece un tratamiento detallado de las dos características principales que diferencian una RS de un MA: la medida del resultado de los estudios (tamaños del efecto, niveles de probabilidad, dirección del efecto) y los métodos de síntesis (cuantitativa) alternativos a los típicos de un MA. A lo largo del tutorial se utiliza un ejemplo para ilustrar las fases de una RS. Finalmente, se ofrecen reflexiones sobre las ventajas y limitaciones de las RS en relación con los MA.

Las revisiones sistemáticas y los meta-análisis son herramientas fundamentales para sintetizar de forma rigurosa la evidencia científica, también en el ámbito educativo. Para que sus resultados sean válidos y útiles, es esencial que se planifiquen, realicen y reporten con precisión metodológica. Este artículo ofrece una guía práctica para desarrollar revisiones sistemáticas y meta-análisis en educación, estructurada en torno a sus fases clave: formulación de la pregunta de investigación, registro del protocolo, búsqueda bibliográfica, selección de estudios, extracción de datos, evaluación de la calidad metodológica, análisis estadístico e interpretación de resultados. A lo largo del texto se presentan recomendaciones concretas y se ilustra el proceso con ejemplos de meta-análisis en el ámbito educativo. Asimismo, se abordan errores frecuentes y se proponen estrategias para prevenirlos. Este enfoque permite comprender tanto los fundamentos teóricos como las decisiones prácticas que requiere este tipo de investigación. En definitiva, las revisiones sistemáticas y los meta-análisis realizados correctamente permiten evaluar la efectividad de las intervenciones educativas, identificar lagunas de conocimiento y orientar la toma de decisiones basada en la mejor evidencia científica disponible. Abstract Systematic reviews and meta-analyses are essential tools for synthesizing scientific evidence, including the field of education. To ensure that their findings are valid, these studies must be carefully planned, rigorously conducted, and transparently reported. This article provides a practical guide to developing systematic reviews and meta-analyses in education, organized around their key phases: formulating the research question, protocol registration, literature search, study selection, data extraction, methodological quality assessment, statistical analyses, and interpretation of the results. Specific recommendations are provided and the process is illustrated with recent examples of meta-analyses in the educational field. Common errors are also addressed, along with strategies to avoid them. This approach enables understanding of both the theoretical foundations and the practical decisions involved in this type of research. Ultimately, well-conducted systematic reviews and meta-analyses enable researchers to evaluate the effectiveness of educational interventions, identify knowledge gaps, and guide decision-making based on the best available scientific evidence.

Las revisiones sistemáticas y los meta-análisis son herramientas fundamentales para sintetizar la evidencia disponible. El crecimiento exponencial de sus publicaciones ha generado una preocupación creciente por la calidad y transparencia de sus informes. La declaración PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) es una guía que facilita la redacción científica, compuesta por una lista de 27 recomendaciones agrupadas en siete apartados clave (Título, Resumen, Introducción, Método, Resultados, Discusión y Financiación). Su objetivo primordial es mejorar la transparencia y claridad de las publicaciones. Su uso se asocia con una mejor calidad y un menor riesgo de sesgos metodológicos, incrementando la legibilidad y la posibilidad de aceptación en revistas de alto impacto. Es crucial comprender que la PRISMA 2020 es una herramienta para la redacción de este tipo de diseños de investigación. Sin embargo, se ha documentado un frecuente uso inadecuado de la PRISMA 2020. Este instrumento no debiera ser utilizado para el diseño de revisiones sistemáticas, ni para evaluar su calidad metodológica o el riesgo de sesgos (funciones para las que existen otros instrumentos como AMSTAR 2), ni para publicar protocolos (para los que se recomienda la PRISMA-P). En conclusión, la PRISMA 2020 es una herramienta específicamente diseñada para asegurar que las revisiones sistemáticas se publiquen de manera rigurosa y transparente, facilitando la comprensión y evaluación por parte de la comunidad científica. Abstract Systematic reviews and meta-analyses are fundamental tools for synthesizing the available evidence. The exponential growth of their publications has generated increasing concern about the quality and transparency of their reports. The PRISMA 2020 statement (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is a guideline that facilitates scientific writing, consisting of a list of 27 recommendations grouped into seven key sections (Title, Abstract, Introduction, Methods, Results, Discussion, and Funding). Its primary goal is to improve the transparency and clarity of publications. Its use is associated with higher quality and lower risk of methodological bias, increasing readability and the likelihood of acceptance in high-impact journals. It is crucial to understand that PRISMA 2020 is a tool for reporting this type of research design. However, frequent inappropriate use of PRISMA 2020 has been documented. This instrument should not be used for designing systematic reviews, nor for assessing their methodological quality or risk of bias (functions for which other instruments, such as AMSTAR 2, exist), nor for publishing protocols (for which PRISMA-P is recommended). In conclusion, PRISMA 2020 is a tool specifically designed to ensure that systematic reviews are published rigorously and transparently, facilitating their comprehension and evaluation by the scientific community.

This study examines the association between problematic internet use, or internet addiction, and adolescent mental health, focusing on key psychological variables, assessing the strength of these associations, and identifying potential moderating factors. Methods: A search of the Web of Science databases over the past five years identified 830 articles. Of these, 33 met the inclusion criteria, involving 303,243 participants (average age 14.57; 49.44% female). The selection process was verified by two researchers. Results: Nine psychological variables were analyzed: depression, anxiety, stress, suicidal behaviour, psychological well-being, self-esteem, externalizing problems, aggressiveness, and impulsiveness. Internet addiction showed positive correlations with aggressiveness (r+ = 0.391), depression (r+ = 0.318), anxiety (r+ = 0.252), and suicidal behaviour (r+ = 0.264). Negative correlations were observed with psychological well-being (r+ = −0.312) and self-esteem (r+ = −0.306). No significant associations were found for externalizing problems, impulsiveness, or stress. None of the moderators showed a significant correlation with internet addiction and depression. Conclusions: Although limited by small sample sizes for some variables and the cross-sectional design of most studies, the findings confirm that there is a negative relationship between internet addiction and adolescent mental health. It is related to poorer self-perceived health, greater psychological distress, and greater aggression.

Background: This study examines the impact of problematic internet use, or internet addiction, on adolescent mental health, focusing on key psychological variables, assessing the strength of these associations, and identifying potential moderating factors. Methods: A search of the Web of Science databases over the past five years identified 830 articles. Of these, 33 met the inclusion criteria, involving 303,243 participants (average age 14.57; 49.44% female). The selection process was verified by two researchers. Results: Nine psychological variables were analysed: depression, anxiety, stress, suicidal behaviour, psychological well-being, self-esteem, externalising problems, aggressiveness, and impulsiveness. Internet addiction showed positive correlations with aggressiveness (r+ = 0.391), depression (r+ = 0.318), anxiety (r+ = 0.252), and suicidal behaviour (r+ = 0.264). Negative correlations were observed with psychological well-being (r+ = -0.312) and self-esteem (r+ = -0.306). No significant associations were found for externalising problems, impulsiveness, or stress. None of the moderators showed a significant correlation with internet addiction and depression. Conclusions: Although limited by small sample sizes for some variables and the cross-sectional design of most studies, the findings confirm that internet addiction negatively affects adolescents' mental health, being linked to poorer self-perceived health, greater psychological distress, and increased aggression.

The relationship between mental disorders other than depression or anxiety, and low adherence to both antiretroviral treatment (ART) and linkage to HIV care are unclear. The aim of our study was to compare the prevalence of mental disorders in people living with HIV (PLHIV) in Spain who present low versus high adherence to ART. We performed a cross-sectional study comparing two groups of PLHIV: 20 PLHIV with low adherence and 80 PLHIV with high adherence to ART. PLHIV who met at least one of the following criteria were included in the low-adherence group: virological failure (HIV-VL > 50 copies/mL in two consecutive blood samples); low attendance to scheduled clinical visits (≥2 missed visits in last year); irregular administration of ART (≥10 forgotten doses in the last month); and interruption of ART for more than 1 week. Inclusion criteria for high adherence were: PLHIV who had been on stable ART for more than 1 year with an HIV-VL below 50 copies/mL and without missed visits over the previous 12 months. The Millon Clinical Multiaxial Inventory was administered to participants. PLHIV with low adherence showed higher scores for all mental disorders compared with those with high adherence. And, in the multivariate binary logistic regression analysis, drug dependence and post-traumatic stress disorder were independently associated with low adherence (Nagelkerke R2 = 0.0686). In conclusion, PLHIV with poorly controlled HIV infection presented important psychological vulnerabilities. Mental health should be checked at the beginning of ART and during follow-up, especially in PLHIV with low adherence or low linkage to the health care system.

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.

The purpose of the study was to assess the effects of advanced HIV disease (AHD) on health-related quality of life (HRQoL) in PLHIV, the changes in HRQoL outcomes over the last 25 years, and the differences between countries according to level of economic development. We conducted a systematic review and meta-analysis. The search was conducted in PubMed and Web of Science using the terms: “health-related quality of life”, “HQRoL”, “HIV”, “AIDS”, “advanced HIV disease” and “low CD4 cells”. Studies inclusion criteria were: adult population; initiated after 1996 and published before July 2021; clinical trials, cross-sectional, cohort, and case–control studies; studies analyzing the relationship between AHD and HRQoL; English or Spanish language. Standardized mean differences (d+) were calculated to estimate the effect size for the meta-analyses. Summary statistics were calculated using a random-effects model, and analyses of effect moderators, using mixed-effects models. The meta-analysis included 38 studies. The results indicated that HRQoL is worse in patients with AHD compared to those without. The main HRQoL domains affected were overall health perception and concern and physical and functional health and symptoms. We found a moderate impact for age and gender on some HRQoL domains. There were no differences in relation to socioeconomic inequities, country of residence, or time period analyzed. In conclusion, advanced HIV disease has a negative impact on health and well-being in PLHIV. Our results show that despite all the advances in antiretroviral treatments over the last 25 years, AHD persists as a source of extreme vulnerability, regardless of where PLHIV live.