An instrument for assessing advanced nursing informatics competencies
Bibliographic record
Abstract
Background/Objective: Researchers set out to develop reliable, valid instruments for nurses to self-assess nursing informatics (NI) competencies at the basic and advanced levels. The focus of the research presented in this article is measurement of competencies at the advanced level, which includes Level 3, the informatics specialist and Level 4, the informatics innovator. Informatics competencies are critical in the technology-rich healthcare delivery system. Nurse leaders experienced in informatics need to be prepared to consistently mentor nurses to use health information technology (HIT) in ways that foster continual growth in nursing informatics competencies. This article addresses the research problem, the concept of competency, previous work on NI assessment, instrument development, and pilot results. Methods: Resulting items from round one and two were reworded to reflect measurable behaviors then subjected to a third round of reviews to establish content validity, using the content validity index (CVI). The Nursing Informatics Competency Assessment L3/L4 (NICA - L3/L4) © instrument development began with a synthesis of seminal and current literature. Participants were asked to rate themselves in one of the categories for each item: beginner or N/A, comfortable, proficient or expert. The NICA-L3/L4© instrument was piloted following Institutional Review Board (IRB) approval using a purposeful, convenience sample from the NI community. Results: For NICA-L3/L4©, the CVIs demonstrated strong content validity and the Chronbach’s alpha showed high internal consistency. The initial data from both the Delphi and pilot studies indicated the need for self-assessment of NI competencies. Conclusion: Results of this study indicate that continued education in NI is necessary to reach the level of nurse innovator, a Level 4 competency. As the healthcare system continues to rely on electronic means of gathering, storing, and retrieving data, self-assessment of informatics competencies is key to providing a benchmark for the identification of skills that require further development.
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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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".