A Technology-Supported Guidance Model to Increase the Flexibility, Quality, and Efficiency of Nursing Education in Clinical Practice in Norway: Development Study of the TOPP-N Application Prototype
Bibliographic record
Abstract
BACKGROUND: The challenges of nursing shortage in the nursing profession and of limited nursing educational capacity in nursing education in clinical practice need to be addressed to ensure supply according to the demand of these professionals. In addition, communication problems among nursing students, nurse educators, and nurse preceptors; variations in the guidance competence of nurse preceptors; and limited overview from nurse educators on nursing students' clinical practice are common challenges reported in several research studies. These challenges affect the quality of nursing education in clinical practice, and even though these problems have been highlighted for several years, a recent study showed that these problems are increasing. Thus, an approach is required to ensure the quality of nursing education in clinical practice. OBJECTIVE: We aimed to develop a guidance and assessment application to meet the challenges reported in clinical practice. The application intended to increase the flexibility, quality, and efficiency of nursing education in clinical practice. Furthermore, it intended to increase interactive communication that supports guidance and ensure structured evaluation of nursing students in clinical practice. METHODS: This study employed a multidisciplinary user-participatory design. Overall, 23 stakeholders from the project team (ie, 5 researchers, 2 software developers, 1 pedagogical advisor, and 15 user representatives [4 educators, 6 preceptors, and 5 students]) participated in a user-centered development process that included workshops, intervention content development, and prototype testing. RESULTS: This study resulted in the creation of the Technology-Optimized Practice Process in Nursing (TOPP-N) guidance and assessment application for use as a supportive tool for nursing students, nurse preceptors, and nurse educators in clinical practice. The development process included the application's name and logo, technical architecture, guidance and assessment module, and security and privacy. CONCLUSIONS: This study offers insights into the development of an evidence-based technological tool to support nursing students, nurse preceptors, and nurse educators in clinical practice. Furthermore, the developed application has the potential to meet several challenges reported in nursing education in clinical practice. After a rigorous development process, we believe that the TOPP-N guidance and assessment application prototype is now ready to be tested in further intervention studies.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".