The examination of nursing work through a role accountability framework
Bibliographic record
Abstract
AIM: To use work analysis data to describe the amount of time registered nurses (RNs) and health care aides (HCA) spent on key clinical role accountabilities and other work activities. BACKGROUND: Health care providers are not effectively utilized. To improve their efficiency and effectiveness, it is necessary to understand how nursing providers enact their role accountabilities. METHOD: Using palm pilot Function Analysis technology, observers recorded the activities of 35 registered nurse and 17 health care aides shifts on a second-by-second basis over 5 days. Work activities were classified using the Nursing Role Effectiveness Model, which conceptualizes nursing practice in terms of clinical role accountabilities. RESULT: The registered nurses spent a considerable amount of time on bio-medical assessment/surveillance, relatively little time was spent on patient and family psycho-social-cultural-spiritual assessment/surveillance and support. CONCLUSION: Unlike other work sampling studies, this research project examined nursing work within a role accountability framework; an important first step in the call for the measurement of the impact of nursing care. IMPLICATIONS FOR NURSING MANAGEMENT: Changes to how registered nurses and health care aides enact their role will require a clear vision by unit managers and their staff of their role accountabilities, and the gap between ideal and actual practice.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".