Evaluating a Serious Gaming Electronic Medication Administration Record System Among Nursing Students: Protocol for a Pragmatic Randomized Controlled Trial (Preprint)
Bibliographic record
Abstract
BACKGROUND Although electronic medication administration record systems have been implemented in settings where nurses work, nursing students commonly lack robust learning opportunities to practice the skills and workflow of digitalized medication administration during their formative education. As a result, nursing students’ performance in administering medication facilitated by technology is often poor. Serious gaming has been recommended as a possible intervention to improve nursing students’ performance with electronic medication administration in nursing education. OBJECTIVE The objectives of this study are to examine whether the use of a gamified electronic medication administration simulator (1) improves nursing students’ attention to medication administration safety within simulated practice, (2) increases student self-efficacy and knowledge of the medication administration process, and (3) improves motivational and cognitive processing attributes related to student learning in a technology-enabled environment. METHODS This study comprised the development of a gamified electronic medication administration record simulator and its evaluation in 2 phases. Phase 1 consists of a prospective, pragmatic randomized controlled trial with second-year baccalaureate nursing students at a Canadian university. Phase 2 consists of qualitative focus group interviews with a cross-section of nursing student participants. RESULTS The gamified medication administration simulator has been developed, and data collection is currently under way. CONCLUSIONS If the gamified electronic medication administration simulator is found to be effective, it could be used to support other health professional simulated education and scaled more widely in nursing education programs. CLINICALTRIAL ClinicalTrials.gov NCT03219151; https://clinicaltrials.gov/show/NCT03219151 (Archived by WebCite at http://www.webcitation.org/6yjBROoDt) REGISTERED REPORT IDENTIFIER RR1-10.2196/9601
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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 | 0.040 | 0.044 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.064 | 0.010 |
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".