RN and RPN Decision Making Across Settings
Bibliographic record
Abstract
Nursing decision making was a focus of the Province-Wide Nursing Project (PWNP), a 3-year project to promote best nursing practice. In much of the growing literature on nursing decision making, it is assumed that there are differences in the way RNs and RPNs make decisions. However, there is little scientific evidence to support this assumption. The RN and RPN decision making across settings questionnaire was completed by nurses employed in the 23 agencies of the 4 Participating Complexes taking part in the project. The survey questions were subjected to factor analysis and reduced to five factors. Results revealed measurable differences between the way that RNs and RPNs made decisions. Both RNs and RPNs reported making decisions frequently and experiencing little difficulty in making them. However, there were statistically significant differences in the frequency with which RNs and RPNs perceived they made decisions and the difficulty they found in making them. To plan effective health care, it is important to take account of the strengths of different health care workers. There is a need for further research to investigate the reasons behind the differences revealed in these findings.
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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".