Development and Validation of the Strengths-Based Nursing and Healthcare Leadership Scale
Bibliographic record
Abstract
Introduction: The healthcare system is currently facing significant human resource challenges. Strengths-Based Nursing and Healthcare Leadership (SBNH-L), a unique, value-driven leadership approach, holds great potential in creating healthy workplaces in healthcare. Objective: To develop and validate a scale to measure SBNH-L. Methods: The development and validation of the SBNH-L scale followed a rigorous process including 3 stages: 1) Item generation, 2) Scale development, and 3) Construct validation. For construct validation, a quantitative psychometric design, with two cross-sectional samples, was used (the first sample in February 2021, n = 194 North American healthcare managers and the second sample in April 2022, n = 357 Canadian healthcare workers). Results: The scale showed good psychometric properties (notably, Cronbach’s alphas ranged from .73 to .96) as well as evidence of construct validity; data showed satisfactory fit with the hypothesized 8-factor structure (χ2 = 747.43, df = 224, p<.001), and one-factor long (χ2 = 811.87, df = 252, p <.001) and short versions (χ2 = 97.70, df = 20, p <.001). The scale predicted organizational support (r =.40, p < .01) and work satisfaction of workers (r = .51, p < .01), two key outcomes, beyond other common leadership approaches. Discussion and Conclusion: The SBNH-L Scale is theoretically and structurally strong: the principal component analysis and the confirmatory factorial analyses results aligned with SBNH-L theory and the SBNH-L Scale demonstrated high internal consistency. The scale provides a unique way to tap into the protective potential of SBNH-L and can be used for evaluative and formative purposes of healthcare leaders and their organizations.
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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".