Competency Recommendations for Advancing Nursing Informatics in the Next Decade: International Survey Results
Bibliographic record
Abstract
The IMIA-NIstudents' and emerging professionals' working group conducted a large international survey in 2015 regarding research trends in nursing informatics. The survey was translated into half-a-dozen languages and distributed through 18 international research collaborators' professional connections. The survey focused on the perspectives of nurse informaticians. A total of 272 participants responded to an open ended question concerning recommendations to advance nursing informatics. Five key areas for action were identified through our thematic content analysis: education, research, practice, visibility and collaboration. This chapter discusses these results with implications for nursing competency development. We propose how components of various competency lists might support the key areas for action. We also identify room to further develop existing competency guidelines to support in-service education for practicing nurses, promote nursing informatics visibility, or improve and facilitate collaboration and integration with other professions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".