Actionable companion · Motivation & engagement

Learning with agency: Digital Competence and Career Adaptability Among Nurses: The Parallel Mediating Roles of Technological Self‐Efficacy and Learning Agility

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education, Adult & professional learning. Health-professions learning must be distinguished from K–12 instruction.

Original authors’ abstract, reproduced under the linked source license:

Background The rapid digital transformation of healthcare systems requires nurses to continuously update their competencies to remain effective, adaptable, and resilient. Although digital competence, technological self‐efficacy, and learning agility are recognized as key factors influencing career adaptability, the mechanisms connecting these constructs within nursing contexts remain underexplored. Aim This study investigated the relationship between digital competence and career adaptability among clinical nurses, with a focus on the mediating roles of technological self‐efficacy and learning agility. Methods A cross‐sectional, correlational design was employed, involving 307 nurses recruited from two specialized university hospitals using stratified sampling. Data were collected using validated instruments measuring digital competence, technological self‐efficacy, learning agility, and career adaptability. Partial Least Squares Structural Equation modeling was utilized to test the hypothesized parallel mediation model, following STROBE reporting guidelines. Results Nurses reported a moderate level of digital competence (M = 3.46, SD = 0.42) and technological self‐efficacy (M = 3.64, SD = 0.46), while learning agility and career adaptability were rated high (M = 3.85, SD = 0.47; M = 4.50, SD = 0.43, respectively). Digital competence had significant positive direct effects on career adaptability (β = 0.185, p < 0.01), learning agility (β = 0.184, p < 0.01), and technological self‐efficacy (β = 0.254, p < 0.001). Both learning agility (β = 0.167, p < 0.01) and technological self‐efficacy (β = 0.236, p < 0.001) were also significant predictors of career adaptability. Moreover, digital competence exerted significant indirect effects on career adaptability through technological self‐efficacy (β = 0.060, p < 0.05) and learning agility (β = 0.031, p < 0.05). Conclusion Digital competence emerged as a fundamental determinant of nurses’ career adaptability, operating directly and indirectly through technological self‐efficacy and learning agility. These findings highlight the importance of fostering digital readiness and adaptive learning among nurses. Nursing leaders should prioritize professional development initiatives to enhance workforce adaptability and sustain effective practice in technology‐driven healthcare environments.

What kind of evidence is this?

Intelligent tutoring & adaptive learning · Survey / observational study

Stage: Higher education; Adult & professional learning. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

This study employed a cross‐sectional, correlational design and was reported in accordance with the STROBE guidelines, ensuring transparent, complete, and standardized documentation of the observational research.

Limitations and uncertainty · source passage

Several limitations should be acknowledged when interpreting the results of this study. First, the cross‐sectional design limits the ability to infer causal relationships among the study variables. Although the structural model specifies directional paths based on theoretical assumptions, the observed associations should be interpreted as relational rather than causal. Longitudinal or experimental research designs are therefore recommended to validate the temporal ordering and causal mechanisms underlying the relationships identified in this study.

Design the system before the pilot

Document the learner model, mastery rule, hint sequence and adaptation signal. Compare adaptive routing with the same content in a non-adaptive sequence. Check whether early errors lead to persistent low-expectation pathways.

Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.

A 90-day district pilot

Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Ibrahim Abdullatif Ibrahim; Samar Atef Mounib; Eman Saad Eldesoky; Reda Shehata Elsayed; Mennat Allah G. Abou Zeid; Hala Gaber Elatroush. Digital Competence and Career Adaptability Among Nurses: The Parallel Mediating Roles of Technological Self‐Efficacy and Learning Agility. Journal of Nursing Management (2026-01-01). https://doi.org/10.1155/jonm/7770229

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.