El valor de p del test no es un ‘índice matemático’, es simplemente una frecuencia relativa / The p-value of the test is not a ‘mathematical index’, it is simply a relative frequency

Las revistas científicas más importantes en campos como medicina, biología y sociología publican reiteradamente artículos y editoriales denunciando que un gran porcentaje de médicos no entiende los conceptos básicos del análisis estadístico, lo que favorece el riesgo de cometer errores al interpretar los datos, los hace más vulnerables frente a informaciones falsas y reduce la eficacia de la investigación. Este problema se extiende a lo largo de toda su carrera profesional y se debe, en gran parte, a una enseñanza deficiente en estadística que es común en países desarrollados. En palabras de H. Halle y S. Krauss, ‘el 90% de los profesores universitarios alemanes que usan con asiduidad el valor de p de los test no entiende lo que mide ese valor’. Es importante destacar que los razonamientos básicos del análisis estadístico son similares a los que realizamos en nuestra vida cotidiana y que comprender los conceptos básicos del análisis estadístico no requiere conocimiento matemático alguno. En contra de lo que muchos investigadores creen, el valor de p del test no es un ‘índice matemático’ que nos permita concluir claramente si, por ejemplo, un fármaco es más efectivo que el placebo. El valor de p del test es simplemente un porcentaje.(AU)

Abstract. Leading scientific journals in fields such as medicine, biology and sociology repeatedly publish articles and editorials claiming that a large percentage of doctors do not understand the basics of statistical analysis, which increases the risk of errors in interpreting data, makes them more vulnerable to misinformation and reduces the effectiveness of research. This problem extends throughout their careers and is largely due to the poor training they receive in statistics – a problem that is common in developed countries. As stated by H. Halle and S. Krauss, ‘90% of German university lecturers who regularly use the p-value in tests do not understand what that value actually measures’. It is important to note that the basic reasoning of statistical analysis is similar to what we do in our daily lives and that understanding the basic concepts of statistical analysis does not require any knowledge of mathematics. Contrary to what many researchers believe, the p-value of the test is not a ‘mathematical index’ that allows us to clearly conclude whether, for example, a drug is more effective than a placebo. The p-value of the test is simply a percentage.(AU)

Use of Artificial Intelligence Tools for Research by Medical Students: A Narrative Review.

With the advent of the era of digitalization, a new door has opened for the development of artificial intelligence (AI)-driven tools/algorithms that can help analyse the huge amount of data uploaded onto the cloud. AI-based tools/algorithms have created a niche in the field of research. AI has enabled researchers and practitioners to access and evaluate an enormous number of scientific papers more effectively. This can link similar studies from the past and highlight research gaps, thus accelerating the literature review, evidence generation, and knowledge discovery process. Medical students can obtain help from various AI-based solutions for literature organization and citations. These tools/algorithms facilitate secure information exchange, collaborative research efforts, and communication among multiple research centres. However, AI-driven research requires the guidance and supervision of human experts for better accuracy, coherence, and credibility of the content entering scientific databases. The key objective of this review is to discuss and evaluate various AI-based tools/algorithms and their key features that can assist medical students in medical research.

Perception, practice, and barriers toward research among pediatric undergraduates: a cross-sectional questionnaire-based survey.

BACKGROUND: Scientific research activities are crucial for the development of clinician-scientists. However, few people pay attention to the current situation of medical research in pediatric medical students in China. This study aims to assess the perceptions, practices and barriers toward medical research of pediatric undergraduates. METHODS: This cross-sectional study was conducted among third-year, fourth-year and fifth-year pediatric students from Zhejiang University School of Medicine in China via an anonymous online questionnaire. The questionnaires were also received from fifth-year students majoring in other medicine programs [clinical medicine ("5 + 3") and clinical medicine (5-year)]. RESULTS: The response rate of pediatric undergraduates was 88.3% (68/77). The total sample of students enrolled in the study was 124, including 36 students majoring in clinical medicine ("5 + 3") and 20 students majoring in clinical medicine (5-year). Most students from pediatrics ("5 + 3") recognized that research was important. Practices in scientific research activities are not satisfactory. A total of 51.5%, 35.3% and 36.8% of the pediatric students participated in research training, research projects and scientific article writing, respectively. Only 4.4% of the pediatric students contributed to publishing a scientific article, and 14.7% had attended medical congresses. None of them had given a presentation at a congress. When compared with fifth-year students in the other medicine program, the frequency of practices toward research projects and training was lower in the pediatric fifth-year students. Lack of time, lack of guidance and lack of training were perceived as the main barriers to scientific work. Limited English was another obvious barrier for pediatric undergraduates. Pediatric undergraduates preferred to participate in clinical research (80.9%) rather than basic research. CONCLUSIONS: Although pediatric undergraduates recognized the importance of medical research, interest and practices in research still require improvement. Lack of time, lack of guidance, lack of training and limited English were the common barriers to scientific work. Therefore, research training and English improvement were recommended for pediatric undergraduates.

Barriers Preventing Medical Trainees from Active Participation in Research Activities.

Background: Research has increasingly become important to career progression and a compulsory component in most medical programs. While medical trainees are consistently urged to undertake research endeavors, they frequently encounter obstacles at both personal and organizational levels that impede the pursuit of high-quality research. This study aims to identify the barriers and recommend successful interventions to increase research productivity amongst medical trainees. Methods: A descriptive cross-sectional survey was carried out among interns, residents, and fellows within a single hospital located in the emirate of Abu Dhabi, UAE. The survey included inquiries regarding perceived obstacles hindering engagement in research activities, factors driving motivation for research involvement, and the assessment of how research participation relates to their job in terms of relevance. Results: Fifty-seven medical trainees participated in the survey, reflecting a response rate of 53%. The survey highlighted common obstacles, notably including time constraints, insufficient statistical and methodology training, the weight of other educational commitments, as well as inadequate incentives and rewards. While a majority of participants expressed interest in engaging in research activities, the consensus was that more incentives and increased funding opportunities would significantly encourage their involvement. Conclusion: Implementing successful interventions such as allocating dedicated time for research, facilitating access to research mentors, and organizing training sessions have the potential to be effective strategies in fostering a thriving research culture and subsequently elevating research productivity of medical trainees.

Key Contextual Factors Involved with Participation in Medical and Genomic Screening and Research for African American and Caucasian Americans: A Qualitative Inquiry American Journal of Community Genetics.

Tremendous progress has been made promoting diversity in recruitment for genomic research, yet challenges remain for several racial demographics. Research has cited intertwined fears of racial discrimination and medical mistrust as contributing factors. This study aimed to identify key factors to establishing trust in medical and genomic screening and research among African Americans and White Americans. Participants completed online focus groups and resulting transcripts were analyzed using a qualitative descriptive approach, with content analysis methods based on recommendations by Schreier. Fifteen African Americans and 23 Caucasian Americans participated in the study, 63% of which were female. The mean age of participants was 38.53 (SD = 16.6). The Overarching Theme of Trust is Context Dependent was identified, along with the following five themes describing elements influencing trustworthiness for our participants: 1) Professional Experience, Education, and Training Bolster Trust; 2) Trust Depends on Relationships; 3) Cross-checking Provided Information is Influential in Establishing Trust; 4) Trust is Undermined by Lack of Objectivity and Bias; and 5) Racism is an Embedded Concern and a Medical Trust Limiting Component for African Americans. To effectively address mistrust and promote recruitment of diverse participants, genomic research initiatives must be communicated in a manner that resonates with the specific diverse communities targeted. Our results suggest key factors influencing trust that should be attended to if we are to promote equity appropriately and respectfully by engaging diverse populations in genomic research.

The role of correspondence analysis in medical research.

Correspondence analysis (CA) is a multivariate statistical and visualization technique. CA is extremely useful in analyzing either two- or multi-way contingency tables, representing some degree of correspondence between columns and rows. The CA results are visualized in easy-to-interpret "bi-plots," where the proximity of items (values of categorical variables) represents the degree of association between presented items. In other words, items positioned near each other are more associated than those located farther away. Each bi-plot has two dimensions, named during the analysis. The naming of dimensions adds a qualitative aspect to the analysis. Correspondence analysis may support medical professionals in finding answers to many important questions related to health, wellbeing, quality of life, and similar topics in a simpler but more informal way than by using more complex statistical or machine learning approaches. In that way, it can be used for dimension reduction and data simplification, clustering, classification, feature selection, knowledge extraction, visualization of adverse effects, or pattern detection.