Role and Strategies that Managers can Apply to Support Point-of-Care Nurses’ Use and Adoption of Health Information Technology: A Scoping Review
Bibliographic record
Abstract
Nurse managers play a significant role in the way care is provided by point-of-care nurses. Yet it is unclear what role nurse managers have in supporting nurses in using health information technologies (ITs) specifically. The objectives of this article are to (1) uncover the role of nurse managers in supporting point-of-care nurses to optimally use health ITs and (2) identify strategies that nurse managers can employ to support point-of-care nurses in using these technologies. A scoping review methodology was used, which resulted in the inclusion of 10 relevant articles. Concerning the role of the nurse manager, four themes were identified: (1) decision making, , (2) implementation planning, (3) supporting staff and (4) evaluation. With regard to strategies to support the use of health ITs, two themes were identified: (1) strategies that prepare nurse leaders in their role of supporting point-of-care staff in using health ITs and (2) strategies that directly support point-of-care staff in using health ITs. The results of this review will be of interest to nurse leaders, informatics researchers and nurse informaticians.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".