Five ways to get a grip by incorporating trust into the design and implementation of peer coaching programs
Bibliographic record
Abstract
Peer coaching is a form of faculty development in which faculty improve their teaching skills through collaborative work or peer observation of teaching. As a tool grounded in experiential learning, peer coaching promotes targeted feedback, reflection on action, and collegial exchange to improve teacher self-efficacy and trainee learning outcomes. Nevertheless, faculty developers face challenges in creating sustainable, effective peer coaching programs as faculty fear scrutiny of their teaching practices. Additionally, to promote collegial exchange, faculty (the person observed and peer coach) must trust one another and accept vulnerability. Without attending to trust, faculty developers may find themselves on black ice, designing and implementing ineffective peer coaching programs. In this Black Ice article, we underscore the role of trust in peer coaching and present five ways to help faculty developers get a grip by incorporating trust into the design and implementation of peer coaching programs, optimizing its efficacy.
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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 | 0.124 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".