The 4C’s of influence framework: fostering leadership development through character, competence, connection and culture
Bibliographic record
Abstract
PURPOSE: The purpose of this paper is to describe the 4C's of Infuence framework and it's application to medicine and medical education. Leadership development is increasingly recognised as an integral physician skill. Competence, character, connection and culture are critical for effective influence and leadership. The theoretical framework, "The 4C's of Influence", integrates these four key dimensions of leadership and prioritises their longitudinal development, across the medical education learning continuum. DESIGN/METHODOLOGY/APPROACH: Using a clinical case-based illustrative model approach, the authors provide a practical, theoretical framework to prepare physicians and medical learners to be engaging influencers and leaders in the health-care system. FINDINGS: As leadership requires foundational skills and knowledge, a leader must be competent to best exert positive influence. Character-based leadership stresses development of, and commitment to, values and principles, in the face of everyday situational pressures. If competence confers the ability to do the right thing, character is the will to do it consistently. Leaders must value and build relationships, fostering connection. Building coalitions with diverse networks ensures different perspectives are integrated and valued. Connected leadership describes leaders who are inspirational, authentic, devolve decision-making, are explorers and foster high levels of engagement. To create a thriving, learning environment, culture must bring everything together, or will become the greatest barrier. ORIGINALITY/VALUE: The framework is novel in applying concepts developed outside of medicine to the medical education context. The approach can be applied across the medical education continuum, building on existing frameworks which focus primarily on what competencies need to be taught. The 4C's is a comprehensive framework for practically teaching the leadership for health care today.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".