Evaluation of the effectiveness of a Strengths-Based Nursing and Healthcare Leadership program aimed at building leadership capacity: A concurrent mixed-methods study
Bibliographic record
Abstract
Background: Targeted interventions have been found effective for developing leadership practices in nurses. However, to date, no leadership training program based on the Strengths-Based Nursing and Healthcare Leadership approach exists. Objectives: Demonstrate the effectiveness of a Strengths-Based Nursing and Healthcare Leadership 6-month program designed for nurse and healthcare leaders on leadership capacity and psychological outcomes. Design: Concurrent mixed-methods with nurse and healthcare leaders from five healthcare organisations in Quebec and Ontario (Canada). Settings: Participants were recruited from five Canadian health care organizations: two in Toronto (Ontario) and three in Montreal (Quebec). Participants: A total of 50 nurse leaders and healthcare leaders were included in the quantitative component, and 22 (20 nurse leaders and two healthcare leaders) participated in the qualitative individual interviews. Methods: -tests, and thematic analysis. Results: Quantitative results suggest a significant improvement in terms of leadership capabilities, work satisfaction, and reduction in perceived stress among participants. Three themes emerged from the qualitative data analysis: 1) focus on people's strengths, 2) structure and language based on Strengths-Based Nursing and Healthcare values, and 3) building support networks. Conclusions: The Strengths-Based Nursing and Healthcare Leadership program developed to build the leadership capabilities of nurse and healthcare leaders was found to be effective. The positive impact of the 6-month program was demonstrated. It was also shown that the leadership program can help improve the leadership competencies, well-being, and work satisfaction of participating nurses and healthcare leaders. Implication: This study reinforces the importance of working with educational, research, and healthcare organizations to establish leadership development programs and mentorship opportunities. Future leadership training should use a Strengths-Based Nursing and Healthcare Leadership lens when tackling leadership and stress in the workplace.
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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 | 0.026 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".