Nursing Time and Work in an Acute Rehabilitation Setting
Bibliographic record
Abstract
Nursing staffing has long been recognized as a significant variable in a hospital budget even through the era of increased productivity and efficiency. In addition, patient acuity has been rising, and increasing demands on nursing personnel have been documented. These increased demands have affected nurse staffing, patient outcomes, and nurse retention, all of which have an impact on our healthcare system. Therefore, it is imperative that nursing time and work be examined in the acute rehabilitation setting--a setting in which research has been sparse. To estimate patient acuity, the activities of nursing personnel must be examined to establish timeframes for the care needed by patients. Previous studies have examined time and work according to pre-established patient acuity categories. California has passed legislation that requires mandatory nurse-staffing ratios in response to the concerns about the adequacy of patient care and safety. We did this study to assess the time and work related to patients with different diagnoses that are typically found in a rehabilitation unit. The data collected can be used to develop a patient acuity system. This study sought to identify how nurses spend their time so that hidden costs and important interventions can be addressed by an institution's administration.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".