Approaches for defining and assessing nursing informatics competencies: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this scoping review was to examine and map the literature on defining and assessing nursing informatics competencies for nurses and nursing students. INTRODUCTION: Over the past three decades, nursing informatics competency research has evolved markedly within countries and nursing roles. It is important to examine the available literature on defining and assessing nursing informatics competencies to inform education, clinical practice, policy, and future research. INCLUSION CRITERIA: We considered literature that defined or assessed the concept of nursing informatics competency as a combination of knowledge, skills, and attitudes. This included nursing informatics competencies of nurses and nursing students in a variety of health care or academic settings. METHODS: An extensive search was conducted in Ovid MEDLINE, CINAHL Plus with Full Text via EBSCO, Ovid Embase, Ovid PsycINFO, ProQuest ERIC, Health and Psychosocial Instruments, ProQuest Australian Education Index, ProQuest Education Databases, ProQuest Dissertations and Theses Global, OCLC PapersFirst, Scopus, Web of Science Core Collection, Wiley Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and the JBI Database of Systematic Reviews and Implementation Reports. The initial search was conducted in May 2017 and updated several times. Nursing informatics websites were searched for gray literature, including unpublished research and organizational documents. Additional papers were identified based on a search of reference lists of all the included papers. Neither language nor date restrictions were applied. Two reviewers assessed each of the included papers independently. Data extraction was undertaken using an extraction tool developed specifically for the scoping review objectives. RESULTS: Fifty-two papers were included. Thirty-four papers identified nursing informatics competencies, grouped into four categories: i) nursing informatics competencies for students, entry-level nurses, or generalist nurses; ii) nursing informatics competencies for a specific nursing role; iii) recommendations for consensus on defining core nursing informatics competencies at the international level; and iv) forecasting future nursing informatics competencies as per evolving nursing roles. Eighteen papers reported on nursing informatics competency assessment tools. Results were discussed in a narrative format supported by tables. CONCLUSIONS: This review provided insights to the state of the science on defining and assessing nursing informatics competencies for nurses and nursing students. Several nursing informatics competency lists are available, and despite some variations in domains of nursing informatics competency and indicator statements, they mostly share common themes. This literature demonstrates a heightened awareness of the importance of nursing informatics competency; however, the availability of many lists may be challenging for frontline nursing staff, nursing educators, administrators, researchers, and students to assimilate. Further research is needed to reach a consensus on core domains of nursing informatics competency and associated indicators, preferably per nursing roles, with international involvement and consensus. Additionally, while many nursing informatics competency assessment tools exist, further research is needed to examine psychometric properties of some of these tools.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.159 | 0.123 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.015 | 0.015 |
| Bibliometrics | 0.035 | 0.024 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.059 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".