Insights from staff nurses and managers on unit-specific nursing performance dashboards: a qualitative study
Bibliographic record
Abstract
INTRODUCTION: Performance data can be used to monitor and guide interventions aimed at improving the quality and safety of patient care. To use performance data effectively, nurses need to understand how to interpret and use data in meaningful ways to guide practice. Dashboards are interactive computerised tools that display performance data. In one large, urban teaching hospital in Toronto, Canada, unit-specific dashboards were implemented across the organisation. METHODS: A qualitative study was undertaken to explore the perceptions and experiences of front-line nurses and managers associated with the implementation of a unit-level dashboard. Six units were selected to participate in the study. Data were analysed using a directed content analysis approach. RESULTS: The sample included 56 study participants, including 51 front-line nurses and 5 unit managers. Three key themes emerged around nurses' and unit managers' perspectives on the implementation of unit-specific dashboards. Nurses and managers described that the Care Utilising Evidence dashboard was a visual tool that displayed data on the impact of the nursing care provided to patients. This tool also was used by the nurses and managers to keep track of processes of care and patient outcomes and experiences at a unit level. Further, nurses were able to use performance data to identify quality care improvements specific to their unit. CONCLUSIONS: The results highlight how unit-specific dashboards are being used to monitor performance and drive quality improvement efforts from the perspectives of nurses and unit managers. In practice, nurse leaders may consider investing in dashboards as a quality improvement strategy to optimise the use of performance data at their organisations.
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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 | 0.028 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".