Informatics and Nursing in a Post-Nursing Informatics World: Future Directions for Nurses in an Automated, Artificially-Intelligent, Social-Networked Healthcare Environment
Bibliographic record
Abstract
The increased adoption and use of technology within healthcare and society has influenced the nursing informatics specialty in a multitude of fashions. Namely, the nursing informatics specialty currently faces a range of important decisions related to its knowledge base, established values and future directions - all of which are in need of development and future-proofing. In light of the increased use of automation, artificial intelligence and big data in healthcare, the specialty must also reconceptualize the roles of both nurses and informaticians to ensure that the nursing profession is ready to operate within future digitalized healthcare ecosystems. To explore these goals, the author of this manuscript outlines an examination of technological advancements currently taking place within healthcare, and also proposes implications for the nursing role and the nursing informatics specialty. Finally, recommendations and insights towards how the roles of nurses and informaticians might evolve or be shaped in the growing post-nursing informatics era are presented.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".