Exploring leadership styles and behaviours in the medical field
Bibliographic record
Abstract
Defining leadership has proven to be quite difficult, as no universal definition currently exists. Leadership is a complex concept described by many theories, each taking a different viewpoint on what makes a leader successful. As an aspiring medical student and pediatric physician with a passion for leadership, I sought to understand which leadership theory, if any, has resulted in more success over others when used in a variety of medical settings.\nTo answer this question, I completed an Individual Study course during the Fall 2021 semester. For this course, I conducted research on various leadership topics within a medical context and wrote four papers that summarized my findings and critically reflected on how they could inform my personal leadership style. Research topics included determining if leader-member exchange theory or transformational leadership is more effective; how a leader can employ servant leadership behaviours to improve the confidence of others; and why a leader being friends with their colleagues can make it very difficult for them to fulfill their leadership duties. Results were then interpreted in terms of personal, team, organizational, and patient outcomes. Finally, an informational interview with a prominent Canadian leader in a medical profession was conducted to understand how my research findings apply to real-world scenarios.\nIt became clear from my research that the most effective leadership incorporates concepts from multiple theories to develop one’s own leadership style. In doing so, a leader should understand and develop their personal core values and lead by them daily.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".