Mentors' and supervisors' perspectives regarding newly qualified nurses' practice in digitally enabled workplaces: A qualitative study
Bibliographic record
Abstract
BACKGROUND: Contemporary healthcare environments are becoming increasingly reliant on digital health technologies, presenting new opportunities and challenges for the nursing profession and nurses across practice settings and roles. Little is known about newly qualified Canadian nurses' experiences as they transition from academic settings to digitally enabled healthcare workplaces. OBJECTIVE: To explore (1) perceptions of nurse managers, clinical preceptors and educators regarding newly qualified nurses' practice with digital health, and (2) identify strategies to enhance new nurses' practice with digital health technologies as they transition to the workplace. METHODS: A descriptive qualitative design was used. Fifteen participants representing nurse managers, clinical preceptors, and educators from two Canadian provinces participated in semi-structured interviews. Thematic analysis was applied to analyze the data. RESULTS: Three themes were identified: 1) Onboarding upon joining the workplace, 2) Factors influencing new hires' practice with technology, and 3) Improving the transition experience to the workplace. Newly qualified nurses have strong digital skills and access to technology training; however, they also face challenges that affect their overall transition and practice. Having a broader understanding of digital health during formal education and in the workplace, mentorship and support from mentors and colleagues, user-friendly technologies, and stable nursing practice environments are key for safe practice and can facilitate the transitional experience and professional growth of new nurses. CONCLUSION: Clearly, digital health is here to stay and will further advance in the years to come. Considering global nursing shortages and the demand for a digitally capable workforce, it is imperative to address gaps and challenges that newly qualified nurses and all nurses face when providing care in digitally enabled healthcare environments.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".