Seventy Years of RN Effectiveness: A Database Development Project to Inform Best Practice
Bibliographic record
Abstract
BACKGROUND: The appropriate nursing staff mix is imperative to the provision of quality care. Nurse staffing levels and staff mix vary from country to country, as well as between care settings. Understanding how staffing skill mix impacts patient, organizational, and financial outcomes is critical in order to allow policymakers and clinicians to make evidence-informed staffing decisions. AIMS: This paper reports on the methodology for creation of an electronic database of studies exploring the effectiveness of Registered Nurses (RNs) on clinical and patient outcomes, organizational and nurse outcomes, and financial outcomes. METHODS: Comprehensive literature searches were conducted in four electronic databases. Inclusion criteria for the database included studies published from 1946 to 2016, peer-reviewed international literature, and studies focused on RNs in all health-care disciplines, settings, and sectors. Masters-prepared nurse researchers conducted title and abstract screening and relevance review to determine eligibility of studies for the database. High-level analysis was conducted to determine key outcomes and the frequency at which they appeared within the database. RESULTS: Of the initial 90,352 records, a total of 626 abstracts were included within the database. Studies were organized into three groups corresponding to clinical and patient outcomes, organizational and nurse-related outcomes, and financial outcomes. Organizational and nurse-related outcomes represented the largest category in the database with 282 studies, followed by clinical and patient outcomes with 244 studies, and lastly financial outcomes, which included 124 studies. LINKING EVIDENCE TO ACTION: The comprehensive database of evidence for RN effectiveness is freely available at https://rnao.ca/bpg/initiatives/RNEffectiveness. The database will serve as a resource for the Registered Nurses' Association of Ontario, as well as a tool for researchers, clinicians, and policymakers for making evidence-informed staffing decisions.
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 | 0.278 | 0.456 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.044 | 0.040 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".