Embracing relational competencies in applying the LEADS framework for health-care leaders in transformational change and the COVID-19 pandemic
Bibliographic record
Abstract
PURPOSE: The purpose of this paper is to assess the extent to which the LEADS Framework guided health-care leaders through organizational change and the COVID-19 pandemic in a western Canadian province. DESIGN/METHODOLOGY/APPROACH: A qualitative exploratory inquiry assessed the extent to which health leaders applied competencies that aligned with the LEADS Framework. A purposeful sample of 22 health-care leaders participated in the study representing senior, mid-level and front-line health-care leaders in various health-care organizations to ensure diverse representation of leader competencies. The authors conducted semi-structured interviews to collect the data and used Braun and Clarke's (2006) six-phase approach to guide data analysis. FINDINGS: The analysis suggests that health-care leaders found Engaging with Others and Developing Coalitions were the most critical themes of the LEADS Framework for change management and for navigating the COVID-19 pandemic. Findings reveal that during transformational change and a crisis context, leaders embrace relational approaches to adapt and improve performance in dynamic organizations. PRACTICAL IMPLICATIONS: These findings have implications for a relational approach to improve teamwork and decrease emotional strain; a focus on mobilizing and sharing power with nurses; and educational programs to advance relational and self-management skills, shared leadership, communication, change management, human resource and talent development as critical learning components for current and future health-care leaders. ORIGINALITY/VALUE: The LEADS Framework is used to examine how health-care leaders responded to transformational change in the organization while situated in a pandemic context.
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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 | 0.022 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".