Integration of Neuroleadership and Ottawa Model of Implementation Leadership (O-Mile) in Shaping Millennial Generation Leadership Development
Bibliographic record
Abstract
Millennials now constitute over half of the global workforce, creating pressing demands for leadership approaches that can adapt to rapidly changing organizational environments in the public, healthcare, and education sectors. This study aims to systematically review and integrate two complementary leadership frameworksNeuroleadership, which enhances cognitive–affective skills such as emotional regulation, empathy, and evidence-based decision-making, and the Ottawa Model of Implementation Leadership (O-MILe), which emphasizes task orientation, change management, and relational support to inform millennial leadership development. Using the PRISMA 2020 guidelines, comprehensive searches across Scopus, Web of Science, ProQuest, and SAGE databases identified 247 studies, with 20 meeting the inclusion criteria after quality appraisal using CASP and MMAT tools. Findings reveal that Neuroleadership is primarily applied in corporate contexts to improve self-regulation and strategic decision-making, whereas O-MILe is more prevalent in healthcare and educational settings to facilitate effective change implementation and team support. This review fills a gap in the literature by providing the first integrative analysis of these frameworks for developing adaptive, empathetic, and implementation-focused leadership in millennials. The implications for practice include designing leadership training modules, competency frameworks, and coaching programs that enhance both adaptability and implementation capacity. The integrative approach provides a cross-disciplinary foundation for preparing millennial leaders to address complex organizational challenges, aligning leadership development with the demands of Industry 5.0 and fostering evidence-based, empathetic, and resilient leadership.
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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 | 0.029 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".