Coaching models for leadership development: An integrative review
Bibliographic record
Abstract
Abstract The purpose of this article was to describe and compare coaching models and to address their relevance to the advancement of leadership. Coaching has become a popular strategy for leadership development and change in complex environments. Despite increasing popularity, little evidence describes the necessity and impact of coaching. An integrative literature review from 1996 to 2010, retrieved through seven databases, reference tracking, and consultation with academic networks, led to inclusion of peer‐reviewed articles on coaching models. Themes and critical elements in the selected coaching models were analyzed. The search yielded 1,414 titles. Four hundred twenty‐seven abstracts were screened using inclusion/exclusion criteria, and 56 papers were retrieved for full‐text screening. Ten papers were included: two coaching models from health care settings, seven from business settings, and one from a medical education institution. Critical components of coaching models are: coach–coachee relationship, problem identification and goal setting, problem solving, transformational process, and mechanisms by which the model achieves outcomes. Factors that impact positive coaching outcomes are: coach's role and attributes, selection of coaching candidates and coach attributes, obstacles and facilitators to the coaching process, benefits and drawbacks of external versus internal coaches, and organizational support. The elements of coaching models identified in this review may be used to guide future research on the effectiveness of coaching as a leadership strategy.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".