The Techno-numerate Nurse: Results of a Study Exploring Nursing Student and Nurse Perceptions of Workplace Mathematics and Technology Demands
Bibliographic record
Abstract
In this paper, we report on the findings of a research study that sought to answer the following questions: (i) How do current nursing students’ perceptions compare with those of actual working nurses regarding the mathematics and technology demands involved in nursing?; and, (ii) What types of course structures, content, pedagogy, or other recommendations could more effectively prepare nurses for the realities of the workplace in light of mathematics and technology demands? The study involved online open-response questions and semi-structured interviews. Seventy-six participants, including both 4th-year nursing students (n = 8) and working nurses (n = 68), completed the online component. Three of the practicing nurses, each working in very different healthcare contexts (mental health, neo-natal intensive care, acute care), volunteered to take part in subsequent in-depth interviews to share further insights. No statistically significant differences were found between nursing students’ and working nurses’ perceptions of mathematics and technology preparation for nursing within their undergraduate experiences. Based on the analysis of open-response item data and interview transcripts, we discuss the following emergent themes: math skills required for practice; math admission requirements; math-related course offerings and instructional strategies; technology skills required for practice; technology addressed in nursing programs; and, issues surrounding evidence-based practice and Internet access. The paper concludes with a list of seven recommendations for nurse education programs, as well as suggested directions for future research.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".