Nursing leaders’ perceptions of the impact of the Strengths-Based Nursing and Healthcare Leadership program three months post training
Bibliographic record
Abstract
Development of nursing leadership is necessary to ensure that nurse leaders of the future are well-equipped to tackle the challenges of a burdened healthcare system. In this context, the Strengths-Based Nursing and Healthcare Leadership program was delivered to 121 participants from 5 organizations in Canada in 2021 and 2022. To date, no study used a qualitative approach to explore nursing leaders’ perceptions of a leadership Strengths-Based Nursing and Healthcare Leadership program three months post training. To describe nursing leaders’ perceptions of the impact of the Strengths-Based Nursing and Healthcare Leadership program three months post training. Qualitative descriptive design was used with individual semi-structured interviews. A convenient sample of nurse leaders (n = 20) who had participated in the leadership program were recruited for an individual interview three months post training. The data generated by interviews were analyzed using a method of thematic content analysis. Three themes emerged from the qualitative data analysis related to the leadership program that stayed with participants three months post training: 1) mentorship: a lasting relationship, 2) human connections through Story-sharing, and 3) focus on strengths. Two other themes emerged related to the changes that they have made since attending the program: 1) seeking out different perspectives to work better as a team and 2) create a positive work environment and to show appreciation for their staff. The present study offers evidence of the impact of the Strengths-Based Nursing and Healthcare Leadership program three months post training. This study reinforces the importance of training using a Strengths-Based Nursing and Healthcare Leadership lens when tackling leadership.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".