The 6 Whats Coaching Model: A Practical Guide to Structuring Professional Coaching Conversations
Bibliographic record
Abstract
Coaching practice, which occurs largely in organizational contexts, has traditionally been seen as forward-looking dialogue that moves clients from intentions to goal attainment. Extensive research can be found attesting to the value of coaching experiences for personal and professional development. Yet, with exponential growth in this field, what is represented as coaching may take a wide variety of forms,  thereby obscuring and problematizing what the nature of professional coaching is, especially as articulated by professional coaching organizations. As well, with such diversity in coaching approaches, how can organizations fully appreciate what they are inviting into their environment when they choose to employ coaching as an HRD strategy? The 6 whats model aims to recenter awareness on the essential elements of a coaching conversation in order that the coherence of coaching practice is more consistent and that practice boundaries for this relatively new profession can be reaffirmed. It builds upon historical traditions within the coaching field and articulates the core elements of coaching conversations that are required so that coaching relationships remain within their legitimate domain of professional endeavor.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.025 | 0.020 |
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".