Creating Empowering Conditions for Nurses with Workplace Autonomy and Agency: How Healthcare Leaders Could Be Guided by Strengths-Based Nursing and Healthcare Leadership (SBNH-L)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The COVID-19 pandemic had the unintended consequence of placing nurses in the spotlight because their knowledge and skills were in desperate need. While it will be years until we fully understand the impact that this pandemic has exacted on the nursing workforce, early studies have found that nurses have been traumatized by this event and many intend to leave the profession This seismic event only further exacerbated an already vulnerable and strained nursing workforce that pre-existed worldwide prior to COVID-19. The pandemic also highlighted the many challenges facing nursing leadership, in particular, how to create conditions to maintain and sustain a healthy nursing workforce. Nurses' job satisfaction has emerged as an important predictor of whether nurses remain in an organization and stay in the profession. When examined more closely, job satisfaction has been related to nurses feeling empowered to exercise autonomy over their own practice and having agency. Autonomy and agency, in turn, are affected by their managers' leadership styles. Leaders are instrumental in setting the tone and creating the climate and culture that either values or devalues autonomy and agency. To help leaders create empowering conditions, we have developed a guide for leaders. This guide, based on the value-driven philosophy of leadership called Strengths-Based Nursing and Healthcare Leadership (SBNH-L), is founded on principles of person-centered, empowerment, relationship-focused, and innate capacities (ie, strengths) that are operationalized in eight core values. This guide can be used by leaders as their roadmap to create empowering workplace conditions that value and facilitate nurses' autonomy and agency.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it