Nursing Leadership Perceptions of Clinical Pathways After Transitioning to an Electronic Health Record in the Acute Care Setting
Bibliographic record
Abstract
Background: Both clinical pathways (CPs) and electronic health records (EHRs) increase the quality and efficiency of health care; however, no known studies have examined the integration of CPs into the EHR during an organizational EHR launch. Aim: To understand how nursing leadership perceives the nursing practice changes that accompanied the transition from paper to EHR-based CPs. Methods: A case study design was utilized, focusing on CPs utilized by one acute care unit within a tertiary care organization. Findings: Transfer of paper CPs into an EHR not built for the Canadian health care context proved to be difficult. In the integration process, a single paper document became spread throughout the EHR. EHR-based CPs are not as clear, and represent a larger documentation burden, than their paper counterparts. Conclusion: Nursing agency has been greatly affected by the change in format of CPs. Further exploration of nursing agency regarding CPs is warranted.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".