Nursing Care Delivery Redesign: Using the Right Data to Make the Right Decisions
Bibliographic record
Abstract
BACKGROUND: In British Columbia, the Nursing Policy Secretariat of the Ministry of Health recently issued a series of priority nursing recommendations, including team-based care delivery models. AIM: This paper will describe the data collection and analysis phase of a quality improvement initiative focused on care delivery redesign within three healthcare organizations. The focus of the care delivery redesign was a transition from total nursing care to team-based nursing care. METHODS: Our leadership-academic partnership used the Canadian Nurses Association's "Staff Mix Decision-Making Framework for Quality Nursing Care" to guide data collection and analysis on patient, nurse and organizational factors. Data were collected by nurse-led project teams using a patient needs assessment tool, surveys of nurses' scope of practice and teamwork and an environmental profile tool with nurse demographics and unit/facility-level characteristics. RESULTS: Findings from one organization's pediatric medicine and surgery units are presented in this paper. CONCLUSION: Quality improvement data provide multiple opportunities for proactive human resource planning and professional development. Resources and examples are provided to guide others' redesign efforts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".