Decision Making for Nurse Staffing: Canadian Perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it