Bibliographic record
Abstract
The basis of successful leadership rests in leading and developing your people, not in maintaining the status quo. In transition to a new leadership role, medical leaders will often try to mend historical conflicts and build new and trusting relationships. However, about six months in, old patterns begin to surface and the messiness of leadership rears its ugly head. Leaders must recognize that this is where their leadership begins — with growing their people and leading their teams through the inevitable messiness of leadership. To meet this challenge, leaders must understand the reason they have come to leadership. To enhance team function, they must work to develop their internal self-awareness, an understanding of their own beliefs, values, and emotions, and external self-awareness, an appreciation of the impact of their words and actions on others. This can be amplified by understanding the value of “thinking slow” or looking at problems intentionally, without an automatic or intuitive response. The key is developing a deeper understanding of yourself and what you bring to leadership to support sustainable change in the health care system, and this is where executive coaching can assist medical leaders — to limit the messiness and create a supportive environment for self-reflection and personal development.
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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".