Evaluating competency based education modules in an online nurse practitioner course
Bibliographic record
Abstract
Competency based education (CBE) has been shown to improve academic performance and could help bridge the gap between education and clinical practice. There is a lack of evaluation data for new content added to courses, particularly CBEs and new technology. The aim of the study was to evaluate the use of CBE modules and GoReact technology in an online psychiatric nurse practitioner course. In a quality improvement study, four CBE modules were used to assess knowledge and clinical skills in an online psychiatric assessment course. Knowledge tests were used to assess student knowledge, adaptations of the Student Evaluation of Educational Quality Scale (SEEQ) and the Systems Usability Scale (SUS) were used to evaluate the students’ responses to the CBE modules. Faculty feedback and comparisons from prior years without CBEs were also examined. All students in the course successfully completed the CBE modules for course credit. The majority of the students who completed the surveys had a positive response to the CBEs and GoReact technology. Faculty were satisfied with using CBEs and the technology and overall student performance in the course and subsequent practicum course following the CBEs was the same or improved. CBE modules appear to be an effective and well received method of instruction in online clinical psychiatric nurse practitioner courses.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".