Peer-coaching with health care professionals: What is the current status of the literature and what are the key components necessary in peer-coaching? A scoping review
Bibliographic record
Abstract
BACKGROUND: Peer-coaching has been used within the education field to successfully transfer a high percentage of knowledge into practice. In recent years, within health care, it has been the subject of interest as a method of both student training and staff continuing education as well as a format for knowledge translation. AIMS: To review the literature from health care training and education to determine the nature and use of peer-coaching. METHOD: Due to the status of the literature, a scoping review methodology was followed. From a total of 137 articles, 16 were found to fit the inclusion criteria and were further reviewed. RESULTS: The review highlights the state of the literature concerning peer-coaching within health care and discusses key aspects of the peer-coaching relationship that are necessary for success. CONCLUSIONS: Most research is being conducted in the domains of nursing and medicine within North America. The number of studies has increased in frequency over the past 10 years. Interest in developing the potential of peer-coaching in both health care student education and continuing clinical education of health care professionals has grown. Future directions for research in this quickly developing area are included.
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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 | 0.019 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| 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".