Survey of Managers Regarding Nurses’ Performance of Nonnursing Duties
Bibliographic record
Abstract
BACKGROUND: The performance of nonnursing duties by nursing staff contributes to an already busy workload while taking time away from patient care. OBJECTIVE: This article reports on a process implemented by a large regional health authority in Canada to measure and address nurses' performance of nonnursing duties through a newly created tool. METHODS: Process improvement methodology was used to conduct this project. A measurement tool, the "Non-Nursing Duties Tracking Tool," was designed for frontline nursing staff and patient care attendants to document the performance of tasks classified as clerical, housekeeping, food services, clinical support, and transportation. This article reports on a survey of managers regarding information collected from frontline nurses and patient care attendants regarding their performance of nonnursing duties and actions taken or planned to address this. RESULTS: Tasks were identified that could be delegated to housekeeping, transport, and clerical staff. Both frontline nurses and managers expressed the need for administrative support to realign nonnursing tasks to more appropriate personnel. Although most managers of nurses expressed concern about the support of managers in other departments to make these changes, little resistance was encountered when adequate resources were in place. CONCLUSIONS: The "Non-Nursing Duties Tracking Tool" is a valid instrument to support the assessment of nonnursing direct care duties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".