Executive Coaching for Leadership Development: Experience of Academic Physician Leaders
Bibliographic record
Abstract
PURPOSE: To identify the perceived impact and benefit of executive coaching by a physician coach in the context of their leadership roles. METHOD: A descriptive qualitative inquiry was conducted. Individual semi-structured interviews ex post facto were conducted with physician leaders who completed an executive coaching program during the period 2015-2016.Interviews were transcribed verbatim, and data were analyzed by applying an emergent thematic analysis approach. RESULTS: Five interviews were conducted. Participants were female specialist physicians age 25-50 years with leadership experience that was minimal (one), more than two years (one), five years (one) or greater than 10 years (two). The experiences of the interview participants captured seven themes: isolation, time management, self-doubt, support, productivity, moving forward and workplace culture change/shift. For all participants, executive coaching appeared to positively impact their personal and professional development. There was a high degree of congruence in the experience of the executive coaching program by participants. CONCLUSIONS: The physician leaders who underwent a series of executive coaching sessions had very similar experiences overall. The added professional development tool of executive coaching for specialist physicians may have a significant role in supporting productivity, increasing workplace engagement and transforming the culture of medical practice.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".