Saskatchewan: Improving Patient, Nursing and Organizational Outcomes Utilizing Formal Nurse–Patient Ratios
Bibliographic record
Abstract
The issue of nurse-to-patient ratios has been of significant interest to nurses in Saskatchewan. A commitment to a nurse-to-patient pilot project was articulated in a letter of understanding in the 2005 to 2008 contract between the Saskatchewan Union of Nurses (SUN) and the Saskatchewan Association of Health Organizations. The SUN, the Saskatoon Health Region and the Saskatchewan Ministry of Health formed a partnership to engage in the pilot project, which lasted from November 2008 to March 2011. The project involved the creation of a flexible, dynamic and real-time staffing tool to inform day-to-day nurse staffing decisions on a hospital unit and was based on an adaptation of Curley's Synergy Model. A medical unit at St. Paul's Hospital in Saskatoon was selected for implementation, and all front-line nursing staff as well as unit nursing leaders were involved. A project working group adapted the Synergy-based Patient Scoring Tool (PST), which had been utilized for a recent project in British Columbia, to its own patient population. In April 2010, nurses began assessing each patient on every shift with the goal of determining the most suitable care provider. Patient assignment became based on the holistic assessment of patient needs according to the PST results rather than "geography" (for example, one nurse assigned to a multi-bed unit regardless of the acuity/capability of patients in the unit). Whenever possible, staffing on the unit was increased according to tool calculations.Positive impacts in patient outcomes began to be noted during the final data collection period for the project – nosocomial infection rates showed improvement, and the number of falls per patient-days decreased. As well, patient needs were made more visible through use of the PST, which created non-threatening opportunities for dialogue related to legislated scopes of practice. While longer timelines and larger sample size are needed to measure impacts on retention and recruitment of nurses, nurses in the project demonstrated increased engagement over the study period. The tools and processes developed in this project are adaptable to other patient populations and care settings.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".