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Journal of Korean Medical Science Sep 2023Comprehensive knowledge of quantitative and qualitative research systematizes scholarly research and enhances the quality of research output. Scientific researchers must... (Review)
Review
Comprehensive knowledge of quantitative and qualitative research systematizes scholarly research and enhances the quality of research output. Scientific researchers must be familiar with them and skilled to conduct their investigation within the frames of their chosen research type. When conducting quantitative research, scientific researchers should describe an existing theory, generate a hypothesis from the theory, test their hypothesis in novel research, and re-evaluate the theory. Thereafter, they should take a deductive approach in writing the testing of the established theory based on experiments. When conducting qualitative research, scientific researchers raise a question, answer the question by performing a novel study, and propose a new theory to clarify and interpret the obtained results. After which, they should take an inductive approach to writing the formulation of concepts based on collected data. When scientific researchers combine the whole spectrum of inductive and deductive research approaches using both quantitative and qualitative research methodologies, they apply mixed-method research. Familiarity and proficiency with these research aspects facilitate the construction of novel hypotheses, development of theories, or refinement of concepts.
Topics: Humans; Qualitative Research; Data Collection; Knowledge; Research Design; Writing
PubMed: 37724495
DOI: 10.3346/jkms.2023.38.e291 -
Journal of Medical Internet Research Aug 2023ChatGPT has promising applications in health care, but potential ethical issues need to be addressed proactively to prevent harm. ChatGPT presents potential ethical...
ChatGPT has promising applications in health care, but potential ethical issues need to be addressed proactively to prevent harm. ChatGPT presents potential ethical challenges from legal, humanistic, algorithmic, and informational perspectives. Legal ethics concerns arise from the unclear allocation of responsibility when patient harm occurs and from potential breaches of patient privacy due to data collection. Clear rules and legal boundaries are needed to properly allocate liability and protect users. Humanistic ethics concerns arise from the potential disruption of the physician-patient relationship, humanistic care, and issues of integrity. Overreliance on artificial intelligence (AI) can undermine compassion and erode trust. Transparency and disclosure of AI-generated content are critical to maintaining integrity. Algorithmic ethics raise concerns about algorithmic bias, responsibility, transparency and explainability, as well as validation and evaluation. Information ethics include data bias, validity, and effectiveness. Biased training data can lead to biased output, and overreliance on ChatGPT can reduce patient adherence and encourage self-diagnosis. Ensuring the accuracy, reliability, and validity of ChatGPT-generated content requires rigorous validation and ongoing updates based on clinical practice. To navigate the evolving ethical landscape of AI, AI in health care must adhere to the strictest ethical standards. Through comprehensive ethical guidelines, health care professionals can ensure the responsible use of ChatGPT, promote accurate and reliable information exchange, protect patient privacy, and empower patients to make informed decisions about their health care.
Topics: Humans; Artificial Intelligence; Reproducibility of Results; Data Collection; Disclosure; Patient Compliance
PubMed: 37566454
DOI: 10.2196/48009 -
European Journal of Epidemiology Dec 2023Surveillance and research data, despite their massive production, often fail to inform evidence-based and rigorous data-driven health decision-making. In the age of...
Surveillance and research data, despite their massive production, often fail to inform evidence-based and rigorous data-driven health decision-making. In the age of infodemic, as revealed by the COVID-19 pandemic, providing useful information for decision-making requires more than getting more data. Data of dubious quality and reliability waste resources and create data-genic public health damages. We call therefore for a slow data public health, which means focusing, first, on the identification of specific information needs and, second, on the dissemination of information in a way that informs decision-making, rather than devoting massive resources to data collection and analysis. A slow data public health prioritizes better data, ideally population-based, over more data and aims to be timely rather than deceptively fast. Applied by independent institutions with expertise in epidemiology and surveillance methods, it allows a thoughtful and timely public health response, based on high-quality data fostering trustworthiness.
Topics: Humans; Public Health; Reproducibility of Results; Pandemics; COVID-19; Data Collection
PubMed: 37789225
DOI: 10.1007/s10654-023-01049-6 -
Ugeskrift For Laeger Oct 2023Documenting systematic searches promotes transparency, reproducibility, and integrity in research. In recent years, various reporting guidelines have gained widespread... (Review)
Review
Documenting systematic searches promotes transparency, reproducibility, and integrity in research. In recent years, various reporting guidelines have gained widespread recognition and adoption, and some journals and funders require researchers to provide a detailed account of their search strategies. Documentation of systematic searches vary depending on the search strategies and types of reviews. This review provides an overview of principles of reporting search strategies for key review types and search strategies, and furthermore an overview of existing reporting guidelines.
Topics: Humans; Documentation; Reproducibility of Results; Research Personnel; Data Collection; Systematic Reviews as Topic
PubMed: 37873983
DOI: No ID Found -
Human Reproduction (Oxford, England) Dec 2023What are the data and trends on ART and IUI cycle numbers and their outcomes, and on fertility preservation (FP) interventions, reported in 2019 as compared to previous...
STUDY QUESTION
What are the data and trends on ART and IUI cycle numbers and their outcomes, and on fertility preservation (FP) interventions, reported in 2019 as compared to previous years?
SUMMARY ANSWER
The 23rd ESHRE report highlights the rising ART treatment cycles and children born, alongside a decline in twin deliveries owing to decreasing multiple embryo transfers; fresh IVF or ICSI cycles exhibited higher delivery rates, whereas frozen embryo transfers (FET) showed higher pregnancy rates (PRs), and reported IUI cycles decreased while maintaining stable outcomes.
WHAT IS KNOWN ALREADY
ART aggregated data generated by national registries, clinics, or professional societies have been gathered and analyzed by the European IVF-Monitoring (EIM) Consortium since 1997 and reported in a total of 22 manuscripts published in Human Reproduction and Human Reproduction Open.
STUDY DESIGN, SIZE, DURATION
Data on medically assisted reproduction (MAR) from European countries are collected by EIM for ESHRE each year. The data on treatment cycles performed between 1 January and 31 December 2019 were provided by either national registries or registries based on initiatives of medical associations and scientific organizations or committed persons in one of the 44 countries that are members of the EIM Consortium.
PARTICIPANTS/MATERIALS, SETTING, METHODS
Overall, 1487 clinics offering ART services in 40 countries reported, for the second time, a total of more than 1 million (1 077 813) treatment cycles, including 160 782 with IVF, 427 980 with ICSI, 335 744 with FET, 64 089 with preimplantation genetic testing (PGT), 82 373 with egg donation (ED), 546 with IVM of oocytes, and 6299 cycles with frozen oocyte replacement (FOR). A total of 1169 institutions reported data on IUI cycles using either husband/partner's semen (IUI-H; n = 147 711) or donor semen (IUI-D; n = 51 651) in 33 and 24 countries, respectively. Eighteen countries reported 24 139 interventions in pre- and post-pubertal patients for FP, including oocyte, ovarian tissue, semen, and testicular tissue banking.
MAIN RESULTS AND THE ROLE OF CHANCE
In 21 countries (21 in 2018) in which all ART clinics reported to the registry 476 760 treatment cycles were registered for a total population of approximately 300 million inhabitants, allowing the best estimate of a mean of 1581 cycles performed per million inhabitants (range: 437-3621). Among the reporting countries, for IVF the clinical PRs per aspiration slightly decreased while they remained similar per transfer compared to 2018 (21.8% and 34.6% versus 25.5% and 34.1%, respectively). In ICSI, the corresponding PRs showed similar trends compared to 2018 (20.2% and 33.5%, versus 22.5% and 32.1%) When freeze-all cycles were not considered for the calculations, the clinical PRs per aspiration were 28.5% (28.8% in 2018) and 26.2% (27.3% in 2018) for IVF and ICSI, respectively. After FET with embryos originating from own eggs, the PR per thawing was at 35.1% (versus 33.4% in 2018), and with embryos originating from donated eggs at 43.0% (41.8% in 2018). After ED, the PR per fresh embryo transfer was 50.5% (49.6% in 2018) and per FOR 44.8% (44.9% in 2018). In IVF and ICSI together, the trend toward the transfer of fewer embryos continues with the transfer of 1, 2, 3, and ≥4 embryos in 55.4%, 39.9%, 2.6%, and 0.2% of all treatments, respectively (corresponding to 50.7%, 45.1%, 3.9%, and 0.3% in 2018). This resulted in a reduced proportion of twin delivery rates (DRs) of 11.9% (12.4% in 2018) and a similar triplet DR of 0.3%. Treatments with FET in 2019 resulted in twin and triplet DR of 8.9% and 0.1%, respectively (versus 9.4% and 0.1% in 2018). After IUI, the DRs remained similar at 8.7% after IUI-H (8.8% in 2018) and at 12.1% after IUI-D (12.6% in 2018). Twin and triplet DRs after IUI-H were 8.7% and 0.4% (in 2018: 8.4% and 0.3%) and 6.2% and 0.2% after IUI-D (in 2018: 6.4% and 0.2%), respectively. Eighteen countries (16 in 2018) provided data on FP in a total number of 24 139 interventions (20 994 in 2018). Cryopreservation of ejaculated sperm (n = 11 592 versus n = 10 503 in 2018) and cryopreservation of oocytes (n = 10 784 versus n = 9123 in 2018) were most frequently reported.
LIMITATIONS, REASONS FOR CAUTION
Caution with the interpretation of results should remain as data collection systems and completeness of reporting vary among European countries. Some countries were unable to deliver data about the number of initiated cycles and/or deliveries.
WIDER IMPLICATIONS OF THE FINDINGS
The 23rd ESHRE data collection on ART, IUI, and FP interventions shows a continuous increase of reported treatment numbers and MAR-derived livebirths in Europe. Although it is the largest data collection on MAR in Europe, further efforts toward optimization of both the collection and the reporting, from the perspective of improving surveillance and vigilance in the field of reproductive medicine, are awaited.
STUDY FUNDING/COMPETING INTEREST(S)
The study has received no external funding and all costs are covered by ESHRE. There are no competing interests.
Topics: Pregnancy; Female; Child; Humans; Male; Fertilization in Vitro; Reproductive Techniques, Assisted; Pregnancy Outcome; Semen; Pregnancy Rate; Registries; Pregnancy, Twin; Europe; Retrospective Studies
PubMed: 37847771
DOI: 10.1093/humrep/dead197 -
Ugeskrift For Laeger Jan 2024Qualitative studies are adept at exploring individuals' routines, practices, thoughts, and values, as well as interaction and collaboration. As a doctor, you encounter... (Review)
Review
Qualitative studies are adept at exploring individuals' routines, practices, thoughts, and values, as well as interaction and collaboration. As a doctor, you encounter qualitative research questions daily: Why do patients hesitate to follow recommendations? How do doctors broach sensitive topics with patients? How do fellow physicians experience cross-sector collaboration? This review provides a quick guide to qualitative studies, covering research question formulation, data collection, analysis, and transparency criteria. We critically assess a qualitative study on chronic disease management.
Topics: Humans; Physicians; Qualitative Research; Data Collection
PubMed: 38235777
DOI: 10.61409/V08230491 -
International Journal of Biometeorology Oct 2023The number and diversity of phenological studies has increased rapidly in recent years. Innovative experiments, field studies, citizen science projects, and analyses of... (Review)
Review
The number and diversity of phenological studies has increased rapidly in recent years. Innovative experiments, field studies, citizen science projects, and analyses of newly available historical data are contributing insights that advance our understanding of ecological and evolutionary responses to the environment, particularly climate change. However, many phenological data sets have peculiarities that are not immediately obvious and can lead to mistakes in analyses and interpretation of results. This paper aims to help researchers, especially those new to the field of phenology, understand challenges and practices that are crucial for effective studies. For example, researchers may fail to account for sampling biases in phenological data, struggle to choose or design a volunteer data collection strategy that adequately fits their project's needs, or combine data sets in inappropriate ways. We describe ten best practices for designing studies of plant and animal phenology, evaluating data quality, and analyzing data. Practices include accounting for common biases in data, using effective citizen or community science methods, and employing appropriate data when investigating phenological mismatches. We present these best practices to help researchers entering the field take full advantage of the wealth of available data and approaches to advance our understanding of phenology and its implications for ecology.
Topics: Animals; Humans; Seasons; Climate Change; Trees; Data Collection; Volunteers
PubMed: 37507579
DOI: 10.1007/s00484-023-02502-7 -
The Lancet. Infectious Diseases Sep 2023Novel data and analyses have had an important role in informing the public health response to the COVID-19 pandemic. Existing surveillance systems were scaled up, and in... (Review)
Review
Novel data and analyses have had an important role in informing the public health response to the COVID-19 pandemic. Existing surveillance systems were scaled up, and in some instances new systems were developed to meet the challenges posed by the magnitude of the pandemic. We describe the routine and novel data that were used to address urgent public health questions during the pandemic, underscore the challenges in sustainability and equity in data generation, and highlight key lessons learnt for designing scalable data collection systems to support decision making during a public health crisis. As countries emerge from the acute phase of the pandemic, COVID-19 surveillance systems are being scaled down. However, SARS-CoV-2 resurgence remains a threat to global health security; therefore, a minimal cost-effective system needs to remain active that can be rapidly scaled up if necessary. We propose that a retrospective evaluation to identify the cost-benefit profile of the various data streams collected during the pandemic should be on the scientific research agenda.
Topics: Humans; COVID-19; SARS-CoV-2; Pandemics; Retrospective Studies; Data Collection
PubMed: 37150186
DOI: 10.1016/S1473-3099(23)00121-4 -
Journal of Speech, Language, and... Jul 2023A major barrier to the wider use of language sample analysis (LSA) is the fact that transcription is very time intensive. Methods that can reduce the required time and...
PURPOSE
A major barrier to the wider use of language sample analysis (LSA) is the fact that transcription is very time intensive. Methods that can reduce the required time and effort could help in promoting the use of LSA for clinical practice and research.
METHOD
This article describes an automated pipeline, called Batchalign, that takes raw audio and creates full transcripts in Codes for the Human Analysis of Talk (CHAT) transcription format, complete with utterance- and word-level time alignments and morphosyntactic analysis. The pipeline only requires major human intervention for final checking. It combines a series of existing tools with additional novel reformatting processes. The steps in the pipeline are (a) automatic speech recognition, (b) utterance tokenization, (c) automatic corrections, (d) speaker ID assignment, (e) forced alignment, (f) user adjustments, and (g) automatic morphosyntactic and profiling analyses.
RESULTS
For work with recordings from adults with language disorders, six major results were obtained: (a) The word error rate was between 2.4% for controls and 3.4% for patients, (b) utterance tokenization accuracy was at the level reported for speakers without language disorders, (c) word-level diarization accuracy was at 93% for control participants and 83% for participants with language disorders, (d) utterance-level diarization accuracy based on word-level diarization was high, (e) adherence to CHAT format was fully accurate, and (f) human transcriber time was reduced by up to 75%.
CONCLUSION
The pipeline dramatically shortens the time gap between data collection and data analysis and provides an output superior to that typically generated by human transcribers.
Topics: Adult; Humans; Speech; Language; Language Disorders; Automation; Data Collection
PubMed: 37348510
DOI: 10.1044/2023_JSLHR-22-00642 -
BMC Medical Research Methodology Aug 2023Epidemiologic and medical studies often rely on evaluators to obtain measurements of exposures or outcomes for study participants, and valid estimates of associations...
BACKGROUND
Epidemiologic and medical studies often rely on evaluators to obtain measurements of exposures or outcomes for study participants, and valid estimates of associations depends on the quality of data. Even though statistical methods have been proposed to adjust for measurement errors, they often rely on unverifiable assumptions and could lead to biased estimates if those assumptions are violated. Therefore, methods for detecting potential 'outlier' evaluators are needed to improve data quality during data collection stage.
METHODS
In this paper, we propose a two-stage algorithm to detect 'outlier' evaluators whose evaluation results tend to be higher or lower than their counterparts. In the first stage, evaluators' effects are obtained by fitting a regression model. In the second stage, hypothesis tests are performed to detect 'outlier' evaluators, where we consider both the power of each hypothesis test and the false discovery rate (FDR) among all tests. We conduct an extensive simulation study to evaluate the proposed method, and illustrate the method by detecting potential 'outlier' audiologists in the data collection stage for the Audiology Assessment Arm of the Conservation of Hearing Study, an epidemiologic study for examining risk factors of hearing loss in the Nurses' Health Study II.
RESULTS
Our simulation study shows that our method not only can detect true 'outlier' evaluators, but also is less likely to falsely reject true 'normal' evaluators.
CONCLUSIONS
Our two-stage 'outlier' detection algorithm is a flexible approach that can effectively detect 'outlier' evaluators, and thus data quality can be improved during data collection stage.
Topics: Humans; Computer Simulation; Algorithms; Data Collection; Risk Factors; Data Accuracy
PubMed: 37528402
DOI: 10.1186/s12874-023-01988-4