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BACKGROUND: In-depth interviews are a common method of qualitative data collection, providing rich data on individuals\' perceptions and behaviors that would be challenging to collect with quantitative methods. Researchers typically need to decide on sample size a priori. Although studies have assessed when saturation has been achieved, there is no agreement on the minimum number of interviews needed to achieve saturation. To date, most research on saturation has been based on in-person data collection. During the COVID-19 pandemic, web-based data collection became increasingly common, as traditional in-person data collection was possible. Researchers continue to use web-based data collection methods post the COVID-19 emergency, making it important to assess whether findings around saturation differ for in-person versus web-based interviews.
OBJECTIVE: We aimed to identify the number of web-based interviews needed to achieve true code saturation or near code saturation.
METHODS: The analyses for this study were based on data from 5 Food and Drug Administration-funded studies conducted through web-based platforms with patients with underlying medical conditions or with health care providers who provide primary or specialty care to patients. We extracted code- and interview-specific data and examined the data summaries to determine when true saturation or near saturation was reached.
RESULTS: The sample size used in the 5 studies ranged from 30 to 70 interviews. True saturation was reached after 91% to 100% (n=30-67) of planned interviews, whereas near saturation was reached after 33% to 60% (n=15-23) of planned interviews. Studies that relied heavily on deductive coding and studies that had a more structured interview guide reached both true saturation and near saturation sooner. We also examined the types of codes applied after near saturation had been reached. In 4 of the 5 studies, most of these codes represented previously established core concepts or themes. Codes representing newly identified concepts, other or miscellaneous responses (eg, \"in general\"), uncertainty or confusion (eg, \"don\'t know\"), or categorization for analysis (eg, correct as compared with incorrect) were less commonly applied after near saturation had been reached.
CONCLUSIONS: This study provides support that near saturation may be a sufficient measure to target and that conducting additional interviews after that point may result in diminishing returns. Factors to consider in determining how many interviews to conduct include the structure and type of questions included in the interview guide, the coding structure, and the population under study. Studies with less structured interview guides, studies that rely heavily on inductive coding and analytic techniques, and studies that include populations that may be less knowledgeable about the topics discussed may require a larger sample size to reach an acceptable level of saturation. Our findings also build on previous studies looking at saturation for in-person data collection conducted at a small number of sites.

At a time when developments in computational approaches, often associated with the now much-vaunted terms Machine Learning (ML) and Artificial Intelligence (AI), face increasing challenges in terms of fairness, transparency and accountability, the temptation for researchers to apply mainstream ML methods to virtually any type of data seems to remain irresistible. In this paper we critically examine a recent proposal to apply ML to polygraph screening results (where human interviewers have made a conclusion about deception), which raises several questions about the purpose and the design of the research, particularly given the vacuous scientific status of polygraph-based procedures themselves. We argue that in high-stake environments such as criminal justice and employment practice, where fundamental rights and principles of justice are at stake, the legal and ethical considerations for scientific research are heightened. Specifically, we argue that the combination of ambiguously labelled data and ad hoc ML models does not meet this requirement. Worse, such research can inappropriately legitimise otherwise scientifically invalid, indeed pseudo-scientific methods such as polygraph-based deception detection, especially when presented in a reputable scientific journal. We conclude that methodological concerns, such as those highlighted in this paper, should be addressed before research can be said to contribute to resolving any of the fundamental validity issues that underlie methods and techniques used in legal proceedings.

The basic concepts of research are learned through systematic literature searches which form the basis of a research statement and research topic. Then the research question, hypothesis, aim, and objectives, as well as the experimental design, are developed. Given the context provided, the primary focus is on the importance of adequately training postgraduates and young research investigators in research methodology and project development. It is evident that there is a lack of proper training in these areas, and the rapid expansion of colleges in India exacerbates this issue. To address this, research students must receive comprehensive instruction in scientific research methodology, experimental design, statistics, scientific writing, publishing, and research ethics. Our team has been conducting workshops and symposia for more than two decades to improve the current teaching methods in these areas. Most recently, we organized a series of national and international workshops and seminars in multiple states across India to fortify the core concepts of scientific research for students and faculty members. This report highlights the key aspects of these workshops and the positive outcomes experienced by participants.

Criteria for assessment of the significance of scientific articles are presented. The focus is on research design and methodology, illustrated by the classical study on prehospital volume treatment of severely injured individuals with penetrating torso injuries by Bickell et al. (1994). A well-thought out research design is crucial for the success of a scientific study and is documented in a study protocol beforehand. A hypothesis is a provisional explanation or prediction and must be testable, falsifiable, precise, and relevant. There are various types of randomization methods, with the randomized controlled trial being the gold standard for clinical interventional studies. When reading a scientific article it is important to verify whether the research design and setting align with the research question and whether potential sources of error have been considered and controlled. Critical scrutiny should also be applied to references, the funding and expertise of the researchers.
UNASSIGNED: Kriterien zur Einordnung der Aussagekraft wissenschaftlicher Artikel werden vorgestellt. Der Fokus liegt auf Forschungsdesign und Methodik, die anhand der klassischen Studie zur prähospitalen Volumentherapie bei Schwerverletzten mit penetrierenden Torsoverletzungen von Bickell et al. (1994) erläutert werden. Ein sorgfältig durchdachtes Forschungsdesign entscheidet über den Erfolg einer wissenschaftlichen Studie und wird vorab im Studienprotokoll festgehalten. Eine Hypothese ist eine vorläufige Erklärung oder Vorhersage und muss test-, falsifizierbar, präzise und relevant sein. Es gibt verschiedene Randomisierungsarten. Die randomisierte kontrollierte Studie ist der Goldstandard klinischer Interventionsstudien. Beim Lesen einer wissenschaftlichen Arbeit sollte kontrolliert werden, ob Forschungsdesign und -setting zur Forschungsfrage passen, und ob Fehlerquellen bedacht und kontrolliert wurden. Kritisch prüfen lassen sich auch Literaturangaben, Finanzierung und Expertise der Forschenden.

OBJECTIVE: Different tools to assess the potential risk of bias (RoB) for cross-sectional studies have been developed, but it is unclear whether all pertinent bias concepts are addressed. We aimed to identify RoB concepts applicable to cross-sectional research validity and to explore coverage for each in existing appraisal tools.
METHODS: This scoping review followed the Joanna Briggs Institute methodology. We included records of any study design describing or reporting methods, concepts or tools used to consider RoB in health research reported to be descriptive/prevalence survey or analytic/association (cross-sectional) study designs. Synthesis included quantitative and qualitative analysis.
RESULTS: Of the 4556 records screened, 90 were selected for inclusion; 67 (74%) described the development of, or validation process for, appraisal tools, 15 (17%) described methodological content or theory relevant to RoB for cross-sectional studies and 8 (9%) records of methodological systematic reviews. Review of methodological reports identified important RoB concepts for both descriptive/prevalence and analytic/association studies. Tools identified (n = 64 unique tools) were either intended to appraise quality or assess RoB in multiple study designs including cross-sectional studies (n = 21; 33%) or cross-sectional designs alone (n = 43; 67%). Several existing tools were modified (n = 17; 27%) for application to cross-sectional studies. The RoB items most frequently addressed in the RoB tools were validity and reliability of the exposure (53%) or outcome (65%) measurement and representativeness of the study population (59%). Most tools did not consider nonresponse or missingness appropriately or at all.
CONCLUSIONS: Assessing cross-sectional studies involve unique RoB considerations. We identified RoB tools designed for broad applicability across various study designs as well as those specifically tailored for cross-sectional studies. However, none of the identified tools comprehensively address all potential biases pertinent to cross-sectional studies. Our findings indicate a need for continued improvement of RoB tools and suggest that the development of context-specific or more precise tools for this study design may be necessary.

To inform public health policymakers that the generation of local evidence-based knowledge is key. Research capacity in low- and middle-income countries (LMIC) to generate medical knowledge is often weak and insufficiently resourced and efforts to tackle these challenges are not standardized. Continuous research training can equip researchers with the required knowledge and research skills, but its effectiveness largely depends on the quality and pertinence of the training methods used. We aim to assess the effectiveness of the Cameroon HIV/AIDS Research Forum (CAM-HERO) 2022 Research Methodology and Bioethics Training with the objective to describe the knowledge gained and the self-efficacy of health professionals and clinical scientists. A survey was conducted during the one-day training among health professionals and clinical scientists. Participants took an online self-administered questionnaire before and after the training related to the topics taught. The questionnaire consisted of two parts: 1) 18 Multiple Choice Questions (MCQs) to assess knowledge and 2) Nine items to evaluate self-efficacy using a five-point Likert scale. Mean scores were calculated, analysed, and compared using paired t-test for the pre- and post-test results. A total of 30 participants (57% women) completed the socio-demographic form. The median age (IQR) of participants was 33.5 (13.3) years. We registered 38 respondents for the pre-test and 33 respondents for the post-test. There was a rise in knowledge mean score from 13.0 to 14.8 (p=0.001) and an improvement in the perception of self-efficacy with a mean score increase from 2.9 to 3.7 (p < 0.001). Knowledge and perception of self-efficacy on research methodology improved among participants after the training. These results suggest that the CAM-HERO 2022 training had an immediate positive impact on skills and self-efficacy. Hence, we recommend the implementation of this training on a larger scale, periodically, and with long-term follow-up to evaluate its impact.

BACKGROUND: Patient adherence to medications can be assessed using interactive digital health technologies such as electronic monitors (EMs). Changes in treatment regimens and deviations from EM use over time must be characterized to establish the actual level of medication adherence.
OBJECTIVE: We developed the computer script CleanADHdata.R to clean raw EM adherence data, and this tutorial is a guide for users.
METHODS: In addition to raw EM data, we collected adherence start and stop monitoring dates and identified the prescribed regimens, the expected number of EM openings per day based on the prescribed regimen, EM use deviations, and patients\' demographic data. The script formats the data longitudinally and calculates each day\'s medication implementation.
RESULTS: We provided a simulated data set for 10 patients, for which 15 EMs were used over a median period of 187 (IQR 135-342) days. The median patient implementation before and after EM raw data cleaning was 83.3% (IQR 71.5%-93.9%) and 97.3% (IQR 95.8%-97.6%), respectively (Δ+14%). This difference is substantial enough to consider EM data cleaning to be capable of avoiding data misinterpretation and providing a cleaned data set for the adherence analysis in terms of implementation and persistence.
CONCLUSIONS: The CleanADHdata.R script is a semiautomated procedure that increases standardization and reproducibility. This script has broader applicability within the realm of digital health, as it can be used to clean adherence data collected with diverse digital technologies.

BACKGROUND: In this era of evidence-based medicine, only systematic research can help in providing judicious and precise healthcare to individual patients based on updated knowledge and skills. However, many medical professionals do not feel competent and confident enough to conduct research. One of the reasons could be the lack of a research-based curriculum in undergraduate courses. The National Medical Council has also stressed the need for formal training in research methodology for healthcare professionals. The research methodology workshops help to familiarize the participants with basic, clinical, and translational research required to impart optimum patient care. The objective of our study was to evaluate a research methodology workshop conducted for postgraduate students by assessing the participant\'s knowledge, feedback, and expected impact using Kirkpatrick\'s evaluation model.
METHODS: A quasi-experimental, single-group study was conducted among 132 first-year postgraduate students. The four levels of Kirkpatrick\'s model were applied for evaluation. Feedback forms, scores of the pretest and posttest, quality of the research proposal drafted by the postgraduates for their thesis, and finally successful submission of the research proposal were the components used to evaluate the four levels of outcome of Kirkpatrick\'s model.
METHODS: Data collected were compiled and tabulated into MS Excel. Proportions were calculated for categorical variables and mean and standard deviation (SD) for scores. A comparison of means between pre- and postworkshop scores was made with paired t-test. A value of P < 0.05 was considered statistically significant. Statistical analysis was done using IBM SPSS Statistics version 20.0 software.
RESULTS: Out of 132 participants, 29% (38) were males and 71% (94) were females. The mean ± SD pretest and posttest scores at a 95% confidence interval were 10.55 ± 2.537 and 12.43 ± 2.484, respectively. The difference was found to be statistically significant by paired sample t-test (P < 0.001).
CONCLUSIONS: Participant feedback is vital for improving research methodology workshops. The workshop met the overall requirements of the participants. There was a significant improvement in the knowledge of participants after the workshop completion.

BACKGROUND: Implementation science helps generate approaches to expedite the uptake of evidence in practice. Mixed methods are commonly used in implementation research because they allow researchers to integrate distinct qualitative and quantitative methods and data sets to unravel the implementation process and context and design contextual tools for optimizing the implementation. To date, there has been limited discussion on how to ensure rigor in mixed methods implementation research.
OBJECTIVE: To present Particularity, Engagement, Actionable Inferences, Reflexivity, and Legitimation (PEARL) as a practical tool for understanding various components of rigor in mixed methods implementation research.
METHODS: This methodological discussion is based on a nurse-led mixed methods implementation study. The PEARL tool was developed based on an interpretive, critical reflection, and purposive reading of selected literature sources drawn from the researchers\' knowledge, experiences of designing and conducting mixed methods implementation research, and published methodological papers about mixed methods, implementation science, and research rigor.
CONCLUSIONS: An exemplar exploratory sequential mixed methods study in nursing is provided to illustrate the application of the PEARL tool. The proposed tool can be a useful and innovative tool for researchers and students intending to use mixed methods in implementation research. The tool offers a straightforward approach to learning the key rigor components of mixed methods implementation research for application in designing and conducting implementation research using mixed methods.
CONCLUSIONS: Rigorous implementation research is critical for effective uptake of innovations and evidence-based knowledge into practice and policymaking. The proposed tool can be used as the means to establish rigor in mixed methods implementation research in nursing and health sciences.