Learning together: a cohort approach to organizational leadership development
Bibliographic record
Abstract
Purpose The purpose of this paper is to illustrate the formation of the Leadership Development Initiative (LDI) and to demonstrate how the program was collaboratively tailored to meet the organizational and developmental needs of leaders in the organization, using a learning cohort approach for implementation. Design/methodology/approach This paper describes how the LDI was designed, implemented, and assessed through its various stages of formation. Beginning with theory, a learning cohort approach was envisioned to not only bridge organizational departments by bringing leaders from all divisions to learn together, but would also be more sustainable in the long term. A participatory action research study was used to enhance program development and to ultimately explore the effectiveness of the LDI. Findings The LDI was critical to developing leadership and management competencies/skills, organizational networking, relationship building, and fostering a philosophy of leadership as collaborative visionary practice toward a common goal. Research limitations/implications The conceptual framework of the LDI using a learning cohort approach may provide an approach for further development of leadership programs in other healthcare organizations. Practical implications The LDI demonstrated how internally developed leadership programs can be an effective approach, with evaluation and application of research findings to continually improve and enhance the program, when resources are limited but the desire to learn is not. Originality/value The LDI program is a peer based, cohort approach established through a conceptual framework based on advanced leadership theories and practices.
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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 | 0.048 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".