Towards a comprehensive theory of nurse/patient empowerment: applying Kanter’s empowerment theory to patient care
Bibliographic record
Abstract
AIM: The purpose of this theoretical paper is to propose an integrated model of nurse/patient empowerment that could be used as a guide for creating high-quality nursing practice work environments that ensure positive outcomes for both nurses and their patients. BACKGROUND: There are few integrated theoretical approaches to nurse and patient empowerment in the literature, although nurse empowerment is assumed to positively affect patient outcomes. EVALUATION: The constructs described in Kanter's (1993) work empowerment theory are conceptually consistent with the nursing care process and can be logically extended to nurses' interactions with their patients and the outcomes of nursing care. KEY ISSUES: We propose a model of nurse/patient empowerment derived from Kanter's theory that suggests that empowering working conditions increase feelings of psychological empowerment in nurses, resulting in greater use of patient empowerment strategies by nurses, and, ultimately, greater patient empowerment and better health outcomes. CONCLUSIONS: Empirical testing of the model is recommended prior to use of the model in clinical practice. IMPLICATIONS FOR NURSING MANAGEMENT: We argue that empowered nurses are more likely to empower their patients, which results in better patient and system outcomes. Strategies for managers to empower nurses and for nurses to empower patients are suggested.
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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 | 0.019 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.023 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".