Teaching in the Digital Age—Developing a Support Program for Nursing Education Providers: Design-Based Research
Bibliographic record
Abstract
BACKGROUND: Health care systems and the nursing profession worldwide are being transformed by technology and digitalization. Nurses acquire digital competence through their own experience in daily practice, but also from education and training; nursing education providers thus play an important role. While nursing education providers have some level of digital competence, there is a need for ongoing training and support for them to develop more advanced skills and effectively integrate technology into their teaching. OBJECTIVE: This study aims to develop a needs-based support program for nursing education providers to foster digital competencies and to test this intervention. METHODS: We used a design-based research approach, incorporating iterative development with expert consultation to create and evaluate a support program for nursing education providers. Focus groups were conducted online to assess needs, and thematic content analysis was used to derive key insights. The support program was then refined through expert feedback and subjected to a feasibility and satisfaction test, with participant evaluations analyzed descriptively. RESULTS: Six main categories emerged from the focus groups, highlighting key areas, including the use of digital technology, ongoing support needs, and the current state of digitalization in nursing education. The support program was developed based on these findings, with expert validation leading to adjustments in timing, content prioritization, and platform integration. Preliminary testing showed good overall satisfaction with the support program, although participants suggested improvements in content relevance and digital platform usability. CONCLUSIONS: Although the feasibility test showed high satisfaction with the support program, low participation rates and limited perceived knowledge gain were major concerns. The results suggest that while the program was well received, further refinements, including a focus on competency-based approaches and addressing workplace barriers, are needed to increase participation and effectiveness of such interventions. The findings of this research can be used as a basis for the development of similar programs in other educational and health care contexts.
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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 | 0.048 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".