Research in nursing education and the institutional review board/ethics committee

The reporting of ethical review and ethical considerations in articles published in medical education journals: A literature review

BACKGROUND

Health professions education research (HPE-R) must be ethically sound. Ethical review decisions and other ethical considerations should be clearly reported in journal articles to allow readers to assess the ethical soundness of the research. We explored and evaluated how ethical review decisions and ethical considerations for HPE-R are reported in health professions education (HPE) journal articles.

METHODS

We identified a 1-year sample, for 2020, of eight HPE journals. We systematically assessed the reporting of ethical review and key ethical considerations in the articles in the sample.

RESULTS

The search yielded 2004 articles, of which 955 articles (47.7%) were eligible and were thus assessed. Most (83.4%) of the assessed articles mentioned a review by an ethical review board (ERB). In the category 'research articles', 92% articles reported the outcome of the ethical review. In the category 'other articles' (e.g. educational case reports), reporting of ethical processes was less common (32%). Overall, the reporting of key ethical considerations was limited, although these considerations were more reported in 'research articles' compared with 'other articles'.

CONCLUSIONS

ERB assessments and HPE-R approval was reported in most research articles. This finding is an improvement compared with previous evaluations of ethical research practice in HPE-R. All studies, particularly those that are exempted or not fully reviewed, should describe their key ethical considerations clearly to enable the HPE community to assess the ethical soundness. Our review revealed that the reporting of ethical considerations was limited and deserves attention from the research community.

Strategies for developing faculty confidence and competencies to mentor quality improvement DNP projects

DNP faculty who mentor students in quality improvement (QI) DNP projects often lack essential knowledge of QI principles. The purpose of this article is to guide DNP programs in developing confident and competent faculty mentors for QI DNP projects, facilitating DNP student success. At a multi-campus practice- and research-intensive university, strategies employed to teach College of Nursing faculty essential knowledge of QI principles comprise structural and process components. Structural supports standardize faculty workload, promote potential for collaborative scholarship, and provide instructional and resource support for faculty mentors. Organizational processes facilitate identification of practice sites and meaningful projects. A College of Nursing and university Institutional Review Board collaboration established policy to guide human subjects protection regarding DNP project activity, streamlining and standardizing the process. Library support mechanisms, access to ongoing faculty QI training and resources, and faculty feedback processes to improve QI faculty development are ongoing and sustained. Peer coaching provides continued support for faculty development. Initial process outcomes indicate that implemented strategies are well-received by faculty. The transition to competency-based education provides opportunities to create tools to measure multiple student quality and safety competencies highlighted in Domain 5 of The Essentials: Core Competencies for Professional Nursing Education and inform future directions for faculty development essential to support student success.

Assessing EHR Data for Use in Clinical Improvement and Research

ABSTRACT

Data from electronic health records (EHRs) are becoming accessible for use in clinical improvement projects and nursing research. But the data quality may not meet clinicians' and researchers' needs. EHR data, which are primarily collected to document clinical care, invariably contain errors and omissions. This article introduces nurses to the secondary analysis of EHR data, first outlining the steps in data acquisition and then describing a theory-based process for evaluating data quality and cleaning the data. This process involves methodically examining the data using six data quality dimensions-completeness, correctness, concordance, plausibility, currency, and relevance-and helps the clinician or researcher to determine whether data for each variable are fit for use. Two case studies offer examples of problems that can arise and their solutions.