Development of a master’s student assignment to promote safety and quality improvement in ambulatory settings
Bibliographic record
Abstract
This Master’s level assignment describes an approach to educating adult nurse practitioner students to critically consider potential safety and quality issues in advanced practice ambulatory settings. Eighty-one nurse practitioner students selected a process or protocol that occurred in their clinical setting to analyze for potential error. Root cause analysis was utilized as the examination tool. Following root cause analysis, students developed recommendations that would decrease the likelihood of an error occurring in the future with the chosen process/protocol. The outcome of the analysis was submitted as a paper. Additionally, faculty wanted students to see the linkage of education to practice, and to feel that implementation of recommendations for their identified safety and quality process/protocol could be implemented in practice following graduation. Eighty-four percent of the submitted papers fell into four categories that students believed had a potential for error. The major categories were: medication errors (50.6%), laboratory analysis errors (23.5%), and missed diagnoses (9.9%). The fourth category of “Other” (16%) included various paper topics with two or less in the same area. The importance of the particular processes/protocols selected and analyzed by students for error potential was supported by the similarity of student outcomes to studies published in the literature regarding ambulatory care errors. Additionally, the students communicated through the course evaluation system that the assignment was interesting, promoted safer practice, and had the potential for implementation following graduation.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.016 |
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".