Strengths-Based Nursing and Healthcare Leadership: An Approach to Minimize the Rate of Burnout in Healthcare
Bibliographic record
Abstract
Healthcare workers, especially nurses, are susceptible to burnout at more prevalent rates than the rest of the population. We argue that one way to mitigate rates of burnout in healthcare is by putting a spotlight on the Strengths-Based Nursing and Healthcare Leadership (SBNH-L) approach. Literature on SBNH-L is in its infancy, and no literature to date has looked at this leadership approach in relation to burnout. This study aimed to understand how SBNH-L can foster a culture of inclusion in nursing (and healthcare) and how this can be reflected in the rates of burnout among nurses and other healthcare employees. We surveyed 357 healthcare workers in Canada (144 of whom were nurses) using the AskingCanadians online data collection service. The participants were asked to complete the SBNH-L questionnaire in relation to their manager, a perceived inclusion questionnaire and a self-rated burnout questionnaire. To analyze the data, we conducted a mediation analysis using the PROCESS macro on SPSS. The results showed that inclusion partially mediated the relationship between SBNH-L and emotional exhaustion in the full healthcare sample. These results contribute to findings on the newly developed leadership approach of SBNH-L. They also extend into the clinical world to provide insight into inclusion as a mechanism to mitigate burnout rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".