TIGER-based measurement of nursing informatics competencies: The development and implementation of an online tool for self-assessment
Bibliographic record
Abstract
Background/Objective: The aim of this research was to develop a reliable, valid instrument for self-assessment of perceived nursing informatics (NI) competencies. This article describes the development and validity assessment of the instrument. Informatics competencies are deemed a necessity in today’s technologically-rich healthcare delivery system. Work to identify essential informatics skills commenced shortly after the introduction of information technology into healthcare. In subsequent years, professional organizations and individual experts have established NI competencies needed at various levels of nursing practice, from entry level through advanced practice. The Technology Informatics Guiding Educational Reform (TIGER) Initiative represents one such effort. The TIGER Initiative emerged in 2006 as a grassroots effort dedicated to the preparation of a clinical workforce capable of using information technology and informatics to improve the delivery of healthcare. TIGER quickly organized into several different collaborative groups, including one that identified a set of recommended informatics competencies for nurses in 2009. The TIGER effort listed NI competencies in three areas: basic computer skills, information literacy, and clinical information management but did not operationalize these competencies into an instrument that could be used for assessment purposes. Methods: Three rounds of reviews were conducted. In the first review, the researchers examined TIGER competencies, removing duplicative terms and combining items with similar content. The second and third rounds of reviews were each done by two separate sets of three experts in nursing informatics. During the second round, the list from round one was examined for items to retain or add. Resulting items were reworded to reflect measurable behaviors and then subjected to a third round of reviews to establish content validity, using the content validity index (CVI) methodology. Results: CVIs demonstrated moderate validity for the instrument, and items not deemed relevant to the objective of the instrument were deleted, reducing the number of the items on the instrument. The instrument was piloted by posting the invitation on the online discussion forum of a nursing informatics organization. An additional invitation was extended to a group completing a weekend-long NI course. There were 184 respondents. Most respondents ranked themselves as expert on the majority of items, although a lesser degree of confidence was seen with items related to information literacy Conclusions: TIGER competencies provided a useful foundation for the creation of a feasible online instrument for self-assessment of levels of competency. Fewer respondents identified themselves as expert in information literacy competencies. The instrument developed for this research project could be useful in planning educational opportunities in NI.
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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 | 0.061 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".