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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.291 | 0.452 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.025 | 0.039 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".