Modernizing the Methods and Analytics Curricula for Health Science Doctoral Programs

Landscape Analysis of Public Health Jobs in India to Develop an Evidence-Based Public Health Curriculum

The increase in communicable and non-communicable disease incidence and prevalence, changing population demographics, along with concerns about pandemics, natural disasters, and wars, have highlighted the challenges faced by health systems. The study aims to identify data on publicly posted public health jobs available to applicants eligible to work in India to identify the public health and allied fields workforce needs, skills, and expertise in India. A cross-sectional study was done in June-July 2021. The data was collected from eleven common job portals in India. Descriptive and content analysis was done to identify the most common job titles, educational level preferred/desired, skills, and experience required in the public health jobs in India. In total 382 unique public health and related fields jobs were analyzed. Job postings were most commonly classified as manager (n = 68), officer/lead (n = 61), analyst (n = 49), and consultant (n = 44). Around one-fifth of the jobs were based in Delhi (n = 98, 24%). About a quarter of the job postings required more than 8 years of experience (26%, n = 100). More than half of the job postings mentioned having the knowledge and understanding of data analysis and statistical approaches (n = 116, 64%). Around 15% (n = 193) of the job posting wanted the candidate to have expertise in communication. Skills were classified into various types such as software, technical, and language. Timely assessment of the curriculum should be done to impart skills related to the needs of the employers and prepare a skilled and competent public health workforce to address the 21st century public health challenges.

Enhancement of Online Education to the Teaching Paradigm: Taking Academic Medical Postgraduate Cultivation as an Example

Background

The COVID-19 pandemic posed enormous challenges to postgraduate teaching in 2020. Large-scale and continuous online teaching explorations were introduced to cope with this difficult situation, which incidentally shifted the paradigm of postgraduate teaching.

Purpose

A review of the online teaching of local medical schools for postgraduates was performed to identify the success factors in realizing the practice.

Methods

We retrieved medical postgraduate online teaching publications mainly from the local database, the China National Knowledge Infrastructure (CNKI), via the keywords stated below and then performed a retrospective analysis.

Results

We analyzed key success factors in improving online learning engagement that were considered exclusive to offline classroom teaching, including emotional interaction, the immediacy of communication, and enthusiasm for participation. With these positive effects, the integration of online and offline teaching advantages is beneficial for the initiative of academic medical postgraduates and promotes the construction and development of medical postgraduate education.

Conclusion

Online education can overcome the limitations of time, space, and teaching frequency, with great advantages in terms of flexibility and mobility over traditional classroom teaching. It can effectively cope with difficulties in the education of academic medical postgraduates in challenging times. In the post-pandemic era, blended online and offline teaching approaches continue and will become the new normal pedagogy for the training of medical postgraduate students.

Modeling and prediction of pressure injury in hospitalized patients using artificial intelligence

BACKGROUND

Hospital-acquired pressure injuries (PIs) induce significant patient suffering, inflate healthcare costs, and increase clinical co-morbidities. PIs are mostly due to bed-immobility, sensory impairment, bed positioning, and length of hospital stay. In this study, we use electronic health records and administrative data to examine the contributing factors to PI development using artificial intelligence (AI).

METHODS

We used advanced data science techniques to first preprocess the data and then train machine learning classifiers to predict the probability of developing PIs. The AI training was based on large, incongruent, incomplete, heterogeneous, and time-varying data of hospitalized patients. Both model-based statistical methods and model-free AI strategies were used to forecast PI outcomes and determine the salient features that are highly predictive of the outcomes.

RESULTS

Our findings reveal that PI prediction by model-free techniques outperform model-based forecasts. The performance of all AI methods is improved by rebalancing the training data and by including the Braden in the model learning phase. Compared to neural networks and linear modeling, with and without rebalancing or using Braden scores, Random forest consistently generated the optimal PI forecasts.

CONCLUSIONS

AI techniques show promise to automatically identify patients at risk for hospital acquired PIs in different surgical services. Our PI prediction model provide a first generation of AI guidance to prescreen patients at risk for developing PIs.

CLINICAL IMPACT

This study provides a foundation for designing, implementing, and assessing novel interventions addressing specific healthcare needs. Specifically, this approach allows examining the impact of various dynamic, personalized, and clinical-environment effects on PI prevention for hospital patients receiving care from various surgical services.

Impact of nurse scientist-led digital health interventions on management of chronic conditions

BACKGROUND

Chronic conditions are a major contributor to the burden of disease worldwide. Using innovative digital health technologies, nurse scientists are well-positioned to lead efforts to reduce the burden of chronic conditions on individuals, communities, and systems.

PURPOSE

The purpose of this review was to summarize the outcomes commonly measured in nurse-led digital health interventions that target chronic conditions and pose recommendations for the education of future nurse scientists to lead these studies.

METHODS

A rapid review of the literature was completed using CINAHL and Ovid Emcare. Studies were included if the research: a) was led by a nurse; b) described outcomes of a digital health intervention; and c) included any population with a prevalent chronic condition.

FINDINGS

26 studies were included in this review. Nurse-led digital health interventions are being used to support and manage a range of chronic conditions in varied settings.

DISCUSSION

Digital health interventions are changing the delivery of healthcare for individuals living with chronic conditions. These interventions are bridging the gaps between the digital and physical worlds and are rapidly evolving.

CONCLUSION

The recommendations posed in this review reiterate the importance of robust content and methods education for nurse scientists to address future research needs in a digital era.

Metabolomics Research Conducted by Nurse Scientists: A Systematic Scoping Review

Metabolomics, one of the newest omics, allows for investigation of holistic responses of living systems to myriad biological, behavioral, and environmental factors. Researcher use metabolomics to examine the underlying mechanisms of clinically observed phenotypes. However, these methods are complex, potentially impeding their uptake by scientists. In this scoping review, we summarize literature illustrating nurse scientists' use of metabolomics. Using electronic search methods, we identified metabolomics investigations conducted by nurse scientists and published in English-language journals between 1990 and November 2019. Of the studies included in the review (N = 30), 9 (30%) listed first and/or senior authors that were nurses. Studies were conducted predominantly in the United States and focused on a wide array of clinical conditions across the life span. The upward trend we note in the use of these methods by nurse scientists over the past 2 decades mirrors a similar trend across scientists of all backgrounds. A broad range of study designs were represented in the literature we reviewed, with the majority involving untargeted metabolomics (n = 16, 53.3%) used to generate hypotheses (n = 13, 76.7%) of potential metabolites and/or metabolic pathways as mechanisms of clinical conditions. Metabolomics methods match well with the unique perspective of nurse researchers, who seek to integrate the experiences of individuals to develop a scientific basis for clinical practice that emphasizes personalized approaches. Although small in number, metabolomics investigations by nurse scientists can serve as the foundation for robust programs of research to answer essential questions for nursing.