Building a supportive clinical learning environment: Orienting newly licensed nurses to their impact on the professional development of physician trainees
Bibliographic record
Abstract
WHAT WAS THE EDUCATIONAL CHALLENGE?: Nurses play an essential role in the professional development of physician trainees within the clinical learning environment (CLE), but rarely receive formal training regarding this role. WHAT WAS THE SOLUTION?: Utilizing a multifaceted, systematic approach, we developed an educational program for newly licensed nurses which addressed their role in the CLE and the professional development of physician trainees. HOW WAS THE SOLUTION IMPLEMENTED?: We delivered two 90-minute workshops to approximately 40 nurses during the 2021-2022 academic year. Participants completed workshop session evaluations and the Clinical Learning Environment Quick Survey (CLEQS). Data were descriptively analyzed. Workshops were positively received, with most participants rating them as very good/excellent (Workshop #1: 83.3% and Workshop #2: 72.2%). The CLEQS results suggested that the participants' CLEs were predominantly healthy and supportive, with most respondents indicating that they would recommend their unit to colleagues (before Workshop #1: 92.2% and after Workshop #2: 100.0%). WHAT LESSONS WERE LEARNED THAT ARE RELEVANT TO A WIDER GLOBAL AUDIENCE?: Our educational program acknowledges the important role nurses play in the professional development of physician trainees, and equips them with tools to promote teamwork, communication, and a growth mindset towards interactions with physician trainees. WHAT ARE THE NEXT STEPS?: We continue to iterate our interactive workshops to prepare nurses for their important role in the professional development of physician trainees. To employ more active learning strategies, we developed pre-workshop videos. Thus far, we have delivered these workshops to nearly 600 nurses.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".