Centres for Leadership: a strategy for academic integration
Bibliographic record
Abstract
Purpose The purpose of this paper is to describe how an Academic Health Science Centre, providing pediatric rehabilitation services, research, and education, developed a Centres for Leadership (CfL) initiative to integrate its academic functions and embrace the goal of being a learning organization. Design/methodology/approach Historical documents, tracked output information, and staff members' insights were used to describe the ten-year evolution of the initiative, its benefits, and transformational learnings for the organization. Findings The evolutions concerned development of a series of CfLs, and changes over time in leadership and management structure, as well as in operations and targeted activities. Benefits included enhanced clinician engagement in research, practice-based research, and impacts on clinical practice. Transformational learnings concerned the importance of supporting stakeholder engagement, fostering a spirit of inquiry, and fostering leaderful practice. These learnings contributed to three related emergent outcomes reflecting "way stations" on the journey to enhanced evidence-informed decision making and clinical excellence: enhancements in authentic partnerships, greater innovation capacity, and greater understanding and actualization of leadership values. Practical implications Practical information is provided for other organizations interested in understanding how this initiative evolved, its tangible value, and its wider benefits for organizational collaboration, innovation, and leadership values. Challenges encountered and main messages for other organizations are also considered. Originality/value A strategy map is used to present the structures, processes, and outcomes arising from the initiative, with the goal of informing the operations of other organizations desiring to be learning organizations.
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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 | 0.061 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.029 | 0.024 |
| Open science | 0.006 | 0.051 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".