Nursing skill mix and outcomes: a Singapore perspective
Bibliographic record
Abstract
AIM: To summarize key evidence on nursing skill mix in acute care hospitals and their limitations; and identify the gaps in current literature vis-à-vis Singapore's nursing workforce. BACKGROUND: Nursing skill mix has been theorized to be a factor influencing patient, nurse and organizational outcomes. While there is a growing body of literature explicating associations between nursing skill mix and positive outcomes, the evidence does not as yet provide firm directions in determining the best configuration. In addition, differences in nursing workforce characteristics also make it difficult to apply findings from one healthcare setting to another. CONCLUSIONS: In reviewing key evidence from the United States of America and Canada, this paper highlights three critical gaps in the nursing skill mix literature when examined in the context of Singapore's nursing workforce. Issues related to the interface between local and foreign nurses, the impact of speciality education, and the possible effects that work roles and distribution may have on quality of care need to be further examined. This knowledge should provide a robust evidence base with which to inform national policy on skill mix and maximize nursing resources in order to achieve optimal outcomes.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".