Peer Coaching Groups as an Innovative Tool to Foster Performance and Well-being of the Participants
Bibliographic record
Abstract
Organizations has been using diverse tools to address the needs for the employees' well-being and individual development, investing growing budgets and efforts into this important goals. Peer coaching groups (PCGs) can become a low-cost, inclusive, and adaptive toolset to address these pressing needs. Hundreds of global business communities, Fortune 500 companies, non-profit organizations, and prominent business schools have successfully employed different PCG settings. At the same time, amidst the wide diversity of practitioners' approaches to PCGs, scholarly literature lacks explanation of what factors and why make PCGs effective in fostering the performance, learning, and well-being of the participants. Thus, this panel symposium provides an arena for scholars and practitioners to explore different designs of PCGs: the practitioners will explain their PCGs' designs and engage scholars in a discussion on underlying mechanisms that drive each setting and factors that impact its process and outcomes. As a result, we hope that the audience will learn about the value of PCGs, diverse approaches to designing PCGs, and the theoretical bases of their effectiveness.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".