Bridging Worlds to Lead: A conceptual review with stakeholder consultation to create the LEADS+ Development Model
Bibliographic record
Abstract
Purpose: Healthcare leadership within academic health centres is increasingly complex. To handle this increasing complexity, we need models to support emerging and practicing leaders within health systems. Method: Through stakeholder consultation this conceptual review sought to examine leadership constructs and how they intersect with current leadership practices in academic health centres. The goal was to develop a new model of healthcare leadership development. The authors used sequential iterative cycles of divergent and convergent thinking approaches to explore and synthesize various literature vantage points. Approaches used simulated personas and stories to test the model. Finally, the approach sought feedback from stakeholders (including healthcare leaders, medical educators, leadership developers) to offer refinements. Results: After five rounds of discussion and reformulation, the authors arrived at a new model for leadership development: the LEADS+ Development Model . During their stakeholder consultation stage, they garnered feedback from 29 out of 65 recruited individuals (44.6% response rate). More than a quarter of respondents served as a senior leader in a healthcare network or national society (27.5%, n=8). During the stakeholder consultation stage, participants were invited to indicate their endorsement for the new model using a 10-point scale (10=highest level of endorsement). There was a high level of endorsement: 7.93 (SD 1.7) out of 10. Conclusion: The LEADS+ Development Model is a new model to foster leadership development in academic health centers . In addition to describing leadership development trajectories, this model describes the various leadership and followership paradigms adopted by leaders within health systems.
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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 | 0.065 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".