A call for theory-inspired analysis in qualitative research: Ways to construct different truths in and about healthcare.

Over the last 50 years, there has been significant development of qualitative research and related methods in healthcare. Theoretical frameworks support researchers in selecting appropriate research approaches, procedures and analytical tools. However, the implications of the choice of theory are sparsely elucidated. Based on a text excerpt from a public debate article, the study aimed to show how different theory-inspired analytical perspectives produced varied understandings of the same text. The study presented three subanalyses inspired by Bourdieu's sociological theory, Lazarus and Folkman's psychological theory and utilitarian ethics, respectively. The analyses showed that by using different theoretical analytical perspectives in inductive processes, an immediate interpretation of the text was not obvious. It became possible to spot the underlying meta-theoretical assumptions, as the interpretations were not taken for granted or indisputable. Our analyses suggest that different theoretical lenses lead to different interpretations of the same empirical material, recognising the existence of multiple truths or realities. Thus, utilising a theoretical perspective in inductive analyses can enhance transparency and rigour because the analytical optics are made explicit to the reader. This allows the reader to follow the analysis processes and comprehend from which theoretical starting point a truth arises.

How to Use and Report on p-values.

The use of the p-value in quantitative research, particularly its threshold of "P < 0.05" for determining "statistical significance," has long been a cornerstone of statistical analysis in research. However, this standard has been increasingly scrutinized for its potential to mislead findings, especially when the practical significance, the number of comparisons, or the suitability of statistical tests are not properly considered. In response to controversy around use of p-values, the American Statistical Association published a statement in 2016 that challenged the research community to abandon the term "statistically significant". This stance has been echoed by leading scientific journals to urge a significant reduction or complete elimination in the reliance on p-values when reporting results. To provide guidance to researchers in health professions education, this paper provides a succinct overview of the ongoing debate regarding the use of p-values and the definition of p-values. It reflects on the controversy by highlighting the common pitfalls associated with p-value interpretation and usage, such as misinterpretation, overemphasis, and false dichotomization between "significant" and "non-significant" results. This paper also outlines specific recommendations for the effective use of p-values in statistical reporting including the importance of reporting effect sizes, confidence intervals, the null hypothesis, and conducting sensitivity analyses for appropriate interpretation. These considerations aim to guide researchers toward a more nuanced and informative use of p-values.

Artificial intelligence and illusions of understanding in scientific research.

Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists' visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community's ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI.

Scientific discovery in the age of artificial intelligence.

Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. Both developers and users of AI toolsneed a better understanding of when such approaches need improvement, and challenges posed by poor data quality and stewardship remain. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation.

Attrition rate in infant fNIRS research: A meta-analysis.

Understanding the trends and predictors of attrition rate, or the proportion of collected data that is excluded from the final analyses, is important for accurate research planning, assessing data integrity, and ensuring generalizability. In this pre-registered meta-analysis, we reviewed 182 publications in infant (0-24 months) functional near-infrared spectroscopy (fNIRS) research published from 1998 to April 9, 2020, and investigated the trends and predictors of attrition. The average attrition rate was 34.23% among 272 experiments across all 182 publications. Among a subset of 136 experiments that reported the specific reasons for subject exclusion, 21.50% of the attrition was infant-driven, while 14.21% was signal-driven. Subject characteristics (e.g., age) and study design (e.g., fNIRS cap configuration, block/trial design, and stimulus type) predicted the total and subject-driven attrition rates, suggesting that modifying the recruitment pool or the study design can meaningfully reduce the attrition rate in infant fNIRS research. Based on the findings, we established guidelines for reporting the attrition rate for scientific transparency and made recommendations to minimize the attrition rates. This research can facilitate developmental cognitive neuroscientists in their quest toward increasingly rigorous and representative research.

Financiamiento en investigación en salud: obteniendo financiamiento. Consejos útiles para aumentar las chances de éxito. Parte 2 / Health research funding: obtaining grants. Tips to improve the success rate. Part 2

Esta es la segunda parte de un artículo sobre la búsqueda de financiamiento para un proyecto de investigación. Todo proyecto de investigación requiere una fuente de financiamiento para poder ser llevado adelante. La búsqueda de fondos es una tarea que lleva tiempo y esfuerzo con una baja tasa de éxito. Compartimos algunos consejos que podrían ayudar a aumentar esa tasa de éxito en relación con: 1) cómo reconocer la necesidad de búsqueda de una fuente de financiamiento externo, 2) de dónde provienen los fondos, 3) qué gastos se pueden financiar habitualmente con los fondos y 4) cómo mejorar la escritura y la presentación a una convocatoria. (AU)

This is the second part of our series on searching funds for a research plan. Every research proposal requires a source of funding to be carried out. Looking for funds is a time and effort consuming task with a low success rate. We share some tips that may help to improve that success rate related to (1) how to recognize the need of an external funding source, (2) where the funds are coming from, (3) what costs can be funded and (4) how to improve a proposal writing and submission. (AU)

Planning and Reporting Effective Web-Based RAND/UCLA Appropriateness Method Panels: Literature Review and Preliminary Recommendations.

BACKGROUND: The RAND/UCLA Appropriateness Method (RAM), a variant of the Delphi Method, was developed to synthesize existing evidence and elicit the clinical judgement of medical experts on the appropriate treatment of specific clinical presentations. Technological advances now allow researchers to conduct expert panels on the internet, offering a cost-effective and convenient alternative to the traditional RAM. For example, the Department of Veterans Affairs recently used a web-based RAM to validate clinical recommendations for de-intensifying routine primary care services. A substantial literature describes and tests various aspects of the traditional RAM in health research; yet we know comparatively less about how researchers implement web-based expert panels. OBJECTIVE: The objectives of this study are twofold: (1) to understand how the web-based RAM process is currently used and reported in health research and (2) to provide preliminary reporting guidance for researchers to improve the transparency and reproducibility of reporting practices. METHODS: The PubMed database was searched to identify studies published between 2009 and 2019 that used a web-based RAM to measure the appropriateness of medical care. Methodological data from each article were abstracted. The following categories were assessed: composition and characteristics of the web-based expert panels, characteristics of panel procedures, results, and panel satisfaction and engagement. RESULTS: Of the 12 studies meeting the eligibility criteria and reviewed, only 42% (5/12) implemented the full RAM process with the remaining studies opting for a partial approach. Among those studies reporting, the median number of participants at first rating was 42. While 92% (11/12) of studies involved clinicians, 50% (6/12) involved multiple stakeholder types. Our review revealed that the studies failed to report on critical aspects of the RAM process. For example, no studies reported response rates with the denominator of previous rounds, 42% (5/12) did not provide panelists with feedback between rating periods, 50% (6/12) either did not have or did not report on the panel discussion period, and 25% (3/12) did not report on quality measures to assess aspects of the panel process (eg, satisfaction with the process). CONCLUSIONS: Conducting web-based RAM panels will continue to be an appealing option for researchers seeking a safe, efficient, and democratic process of expert agreement. Our literature review uncovered inconsistent reporting frameworks and insufficient detail to evaluate study outcomes. We provide preliminary recommendations for reporting that are both timely and important for producing replicable, high-quality findings. The need for reporting standards is especially critical given that more people may prefer to participate in web-based rather than in-person panels due to the ongoing COVID-19 pandemic.

Financiamiento en investigación: ¿qué necesito antes de comenzar a buscar una fuente de financiamiento?: parte 1 / Research funding: what do I need before I start looking for a funding source?: part 1

Esta es la primera parte de un artículo sobre la búsqueda de financiamiento para un proyecto de investigación. Esta entrega resume los principales ítems para tener en consideración a la hora de postularse a una convocatoria. Requerimientos del proceso: 1. Tiempo protegido. 2. Propuesta de investigación sólida. 3. Equipo calificado y con experiencia. 4. Definición y organización de actividades. 5. Cronograma de actividades. 6. Estimación de costos. (AU)

This is the first part of an article about finding funding for a research project. This delivery summarizes the main ítems to take into consideration when applying for a call. Process requirements: 1. Protected time. 2. Strong research proposal. 3. Qualified and experienced team. 4. Definition and organization of activities. 5. Schedule of activities. 6. Cost estimate. (AU)

IoT in Elementary School for Everyone - A Research Plan.

We propose a tentative research plan to increase students' mental health in elementary schools by implementing Internet of Things (IoT) technology. The research plan should answer how to support students' mental health using IoT solutions and the critical factors influencing testbeds for IoT solutions with the previously mentioned purpose. Our intended research method is Design Science, which we plan to use stepwise.

Hospital Characteristics and Early Enrollment Trends in the American College of Cardiology Voluntary Public Reporting Program.

Importance: Limited data exist regarding the characteristics of hospitals that do and do not participate in voluntary public reporting programs. Objective: To describe hospital characteristics and trends associated with early participation in the American College of Cardiology (ACC) voluntary reporting program for cardiac catheterization-percutaneous coronary intervention (CathPCI) and implantable cardioverter-defibrillator (ICD) registries. Design, Setting, and Participants: This cross-sectional study analyzed enrollment trends and characteristics of hospitals that did and did not participate in the ACC voluntary public reporting program. All hospitals reporting procedure data to the National Cardiovascular Data Registry (NCDR) CathPCI or ICD registries that were eligible for the public reporting program from July 2014 (ie, program launch date) to May 2017 were included. Stepwise logistic regression was used to identify hospital characteristics associated with voluntary participation. Enrollment trends were evaluated considering the date US News & World Report (USNWR) announced that it would credit participating hospitals. Data analysis was performed from March 2017 to January 2018. Main Outcomes and Measures: Hospital characteristics and participation in the public reporting program. Results: By May 2017, 561 of 1747 eligible hospitals (32.1%) had opted to participate in the program. Enrollment increased from 240 to 376 hospitals (56.7%) 1 month after the USNWR announcement that program participation would be considered as a component of national hospital rankings. Compared with hospitals that did not enroll, program participants had increased median (IQR) procedural volumes for PCI (481 [280-764] procedures vs 332 [186-569] procedures; P < .001) and ICD (114 [56-220] procedures vs 62 [25-124] procedures; P < .001). Compared with nonparticipating hospitals, an increased mean (SD) proportion of participating hospitals adhered to composite discharge medications after PCI (0.96 [0.03] vs 0.92 [0.07]; P < .001) and ICD (0.88 [0.10] vs 0.81 [0.12]; P < .001). Hospital factors associated with enrollment included participation in 5 or more NCDR registries (odds ratio [OR],1.98; 95% CI, 1.24-3.19; P = .005), membership in a larger hospital system (ie, 3-20 hospitals vs ≤2 hospitals in the system: OR, 2.29; 95% CI, 1.65-3.17; P = .001), participation in an NCDR pilot public reporting program of PCI 30-day readmissions (OR, 2.93; 95% CI, 2.19-3.91; P < .001), university affiliation (vs government affiliation: OR, 3.85, 95% CI, 1.03-14.29; P = .045; vs private affiliation: OR, 2.22; 95% CI, 1.35-3.57; P < .001), Midwest location (vs South: OR, 1.47; 95% CI, 1.06-2.08; P = .02), and increased comprehensive quality ranking (4 vs 1-2 performance stars in CathPCI: OR, 8.08; 95% CI, 5.07-12.87; P < .001; 4 vs 1 performance star in ICD: OR, 2.26; 95% CI, 1.48-3.44; P < .001) (C statistic = 0.829). Conclusions and Relevance: This study found that one-third of eligible hospitals participated in the ACC voluntary public reporting program and that enrollment increased after the announcement that program participation would be considered by USNWR for hospital rankings. Several hospital characteristics, experience with public reporting, and quality of care were associated with increased odds of participation.

The new era of quantitative cell imaging-challenges and opportunities.

Quantitative optical microscopy-an emerging, transformative approach to single-cell biology-has seen dramatic methodological advancements over the past few years. However, its impact has been hampered by challenges in the areas of data generation, management, and analysis. Here we outline these technical and cultural challenges and provide our perspective on the trajectory of this field, ushering in a new era of quantitative, data-driven microscopy. We also contrast it to the three decades of enormous advances in the field of genomics that have significantly enhanced the reproducibility and wider adoption of a plethora of genomic approaches.

The first six years of meta-research at PLOS Biology.

Meta-research involves the interrogation of every stage of the research lifecycle, from conception to publication and dissemination. Looking back over the first six years of PLOS Biology Meta-Research Articles highlights the important insights that can be obtained from such "research on research".

Machine Learning and Artificial Intelligence in Pharmaceutical Research and Development: a Review.

Over the past decade, artificial intelligence (AI) and machine learning (ML) have become the breakthrough technology most anticipated to have a transformative effect on pharmaceutical research and development (R&D). This is partially driven by revolutionary advances in computational technology and the parallel dissipation of previous constraints to the collection/processing of large volumes of data. Meanwhile, the cost of bringing new drugs to market and to patients has become prohibitively expensive. Recognizing these headwinds, AI/ML techniques are appealing to the pharmaceutical industry due to their automated nature, predictive capabilities, and the consequent expected increase in efficiency. ML approaches have been used in drug discovery over the past 15-20 years with increasing sophistication. The most recent aspect of drug development where positive disruption from AI/ML is starting to occur, is in clinical trial design, conduct, and analysis. The COVID-19 pandemic may further accelerate utilization of AI/ML in clinical trials due to an increased reliance on digital technology in clinical trial conduct. As we move towards a world where there is a growing integration of AI/ML into R&D, it is critical to get past the related buzz-words and noise. It is equally important to recognize that the scientific method is not obsolete when making inferences about data. Doing so will help in separating hope from hype and lead to informed decision-making on the optimal use of AI/ML in drug development. This manuscript aims to demystify key concepts, present use-cases and finally offer insights and a balanced view on the optimal use of AI/ML methods in R&D.

Addressing Structural Racism in Psychiatry With Steps to Improve Psychophysiologic Research.

Importance: The American Medical Association has acknowledged the public health threat posed by racism in medicine. While clinicians in psychiatry have echoed the sentiment, the research community has largely been silent. Current understanding of the biological domains that underlie psychiatric disorders was historically established by studying White populations, often leaving widely used treatments ineffective for Asian, Black, Hispanic, Indigenous, and other racial and ethnic minority individuals. This article addresses how undersampling of racial and ethnic minority individuals has led to overgeneralized physiological findings, the implications for development of psychiatric treatments, and steps to improve service to racially diverse communities. Observations: Three primary observations regarding differences associated with race and ethnicity have been addressed in the existing psychiatric research: misdiagnosis, medication nonadherence, and treatment efficacy and expression of adverse effects. While cultural factors have been discussed as potential factors associated with these differences, a lack of understanding of physiologic systems may be foundational to each of these issues. Recent evidence points to race differences in psychophysiological measures, likely attributed to factors including the lived experience of racism as opposed to inherent biological differences. This mounting evidence supports a reassessment of existing work to examine potential divergent patterns within racial and ethnic groups. The following strategies may improve understanding of the influence of racism on physiology, allowing clinicians to better address psychiatric symptoms and improve existing treatment approaches. Thus, psychiatric researchers need to (1) understand the historic and current terminology for race and ethnicity and use appropriate terms and categories as defined by sociologists, population health experts, and databases while respecting individuals' right to self-identify, (2) refine research questions, and (3) reexamine research data to determine whether patterns observed in largely White populations can extend to other groups. To appropriately implement these steps, researchers must accept the discomfort that accompanies growth, invite scientists from diverse backgrounds to participate, and use resources to increase diversity in recruitment of study participants. This will require a commitment from funding agencies to provide adequate support to recruit and investigate large, diverse samples. Conclusions and Relevance: To create more suitable medical treatments and improve the quality of care received by those with psychiatric conditions, further discussion is needed surrounding the physiologic toll that racism has had on multiple generations of racial and ethnic minority groups and how that may alter responsivity to biobehavioral interventions. To better inform psychiatric research, the resources provided must be expanded, basic physiologic studies should be replicated with more diverse samples and adequate analyses, and psychiatry scientists must reconsider approaches to clinical research.