LEADS+ Developmental Model: Proposing a new model based on an integrative conceptual review
Bibliographic record
Abstract
Purpose: Leaders in academic health sciences centres (AHCs) must navigate multiple roles as an inherent component of their positions. Changing accountabilities, varying expectations, differing leadership competencies required of multiple leadership roles can be exacerbated by health system disruption, such as during the COVID-19 pandemic. We need improved models that support leaders in navigating, so they can better handle the complexity of multiple leadership roles. Method: This integrative conceptual review sought to examine leadership and followership constructs and how they intersect with current leadership practices in AHCs. The goal was to develop a refined model of health care leadership development. The authors used iterative cycles of divergent and convergent thinking to explore and synthesize various literature and existing leadership frameworks. The authors used simulated personas and stories to test the model and, finally, the approach sought feedback from knowledge users (including health care leaders, medical educators, and leadership developers) to offer refinements. Results: After five rounds of discussion and reformulation, the authors arrived at a refined model: the LEADS+ Developmental Model . The model describes four nested stages, organizing progressive capabilities, as an individual toggles between followership and leadership. During the consultation stage, feedback from 29 out of 65 recruited knowledge users (44.6% response rate) was acquired. More than a quarter of respondents served as a senior leader in a health care network or national society (27.5%, n=8). Consulted knowledge users were invited to indicate their endorsement for the refined 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+ Developmental Model may help foster development of academic health centre leaders. In addition to clarifying the synergistic dynamic between leadership and followership, this model describes the paradigms adopted by leaders within health systems throughout their development journey.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".