Development of an instructor guide tool: ‘Three Stages of Holistic Debriefing’
Bibliographic record
Abstract
OBJECTIVE: to describe the development of an English and Brazilian Portuguese Holistic Debriefing Tool focused on nursing educator to promote a reflective learning. METHOD: a methodology study, with three phases: integrative literature review; tool development and review of a panel of nursing experts. The literature review tracked a systematic process. For the tool development were used literature review results, Lederman's Debriefing Process and Zabala's learning framework as theoretical referential to promote a reflective learning in High-Fidelity Simulation. The panel of nursing experts analysed the quality of the tool. RESULTS: literature review evidenced gaps about educator pedagogical preparation and indicated no holistic debriefing tool exists which captures formative and summative aspects of debriefing guidance to assist the educator to debrief. Debriefing tool was purposed with two pages: first page were recommended how conduct debriefing and second page is a questions guidance. The tool evaluation was undertaken for a total of three modifications for congruence and concept reader clarity. CONCLUSION: it was proposed a holistic debriefing tool focused on nursing educator. This study provides an overall picture of the process to promote a reflexive learning in High-Fidelity Simulation and to contribute to formal nursing educator training to apply best pedagogical practice.
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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 | 0.066 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".