Time to care: a patient‐centered quality improvement strategy
Bibliographic record
Abstract
Purpose The purpose of this paper is to describe the processes and results of implementing and evaluating the Releasing Time to Care™ (RTC™) model in a 45‐bed Neurosciences unit in a tertiary care hospital in Saskatchewan province of western Canada. Design/methodology/approach Organizational restructuring in healthcare systems has impacted the ability of clinical registered nurses (CRNs) in participation and in influencing the decision making that affect the delivery and outcomes of patient‐centered care. At the same time, CRNs' work has intensified because of increases in patient acuity, technological advances, complexity of care provided to patient families and communities, in addition to the intensifying demands put on by an aging population and dwindling resources. The work reported in this paper shows that significant improvements have been made based on the current needs and the change is forever imminent. Establishing solid people connections and networking opportunities proved valuable for current and future exchange of information and knowledge translation. Findings Model implementation resulted in positive narrative and empirical data including: improved patient safety, staff engagement, leadership opportunities and an affirmative shift in organizational culture. Improved patient safety was evidenced by a reduction in falls and decreased medication errors. Originality/value The paper focuses on including the clinical nurse in organizational and system change towards improving patient‐centered quality care. Neurosciences 6300 at Royal University Hospital (RUH) in Saskatoon, was viewed as an RTC™ champion and one of the first to implement and complete the 11‐module toolkit.
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 | 0.035 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".