Decision Making for Nurse Staffing: Canadian Perspectives
Bibliographic record
Abstract
The effectiveness of methods for determining nurse staffing is unknown. Despite a great deal of interest in Canada, efforts conducted to date indicate that there is a lack of consensus on nurse staffing decision-making processes. This study explored nurse staffing decision-making processes, supports in place for nurses, nursing workload being experienced, and perceptions of nursing care and outcomes in Canada. Substantial information was provided from participants about the nurse staffing decision-making methods currently employed in Canada including frameworks for nurse staffing, nurse-to-patient ratios, workload measurement systems, and "gut" instinct. A number of key themes emerged from the study that can form the basis for policy and practice changes related to determining appropriate workload for nursing in Canada. These include the use of (a) staffing principles and frameworks, (b) nursing workload measurement systems, (c) nurse-to-patient ratios, and (d) the need for uptake of evidence related to nurse staffing.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.025 | 0.019 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".