Clinical Nurses’ Understanding of Autonomy
Bibliographic record
Abstract
OBJECTIVE: The purpose of the study was to enable nurse managers to identify strategies to support and enhance autonomous practice based on clinical nurses' understanding of autonomy. BACKGROUND: Findings from an organizational work-life satisfaction survey led a nursing management team to question how clinical nurses understand autonomy. The nursing literature offers inconsistent definitions of autonomy and interchangeable use of related concepts. METHODS: Twelve focus groups involving 43 nurses working in cardiovascular service units discussed instances of satisfaction and dissatisfaction with autonomy in their clinical practice and work life. Verbatim transcripts of group discussions were interpreted by a research team to identify salient examples and descriptions of autonomy. RESULTS: Nurses described autonomy as their ability to accomplish patient care goals in a timely manner by using their knowledge and skills to understand and contribute to the overall plan of care; assess patient needs and conditions; effectively communicate concerns and priorities regarding patient care; and access and coordinate the resources of the multidisciplinary team. CONCLUSIONS: These findings challenge assumptions about autonomy as independent decision making and practice. They highlight nurses' contributions to patient care goals through knowledge of how to get things done within hospital systems and through interdisciplinary coordination and collaboration.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".