Development of an Educational Game to Set Up Surgical Instruments on the Mayo Stand or Back Table: Applied Research in Production Technology
Bibliographic record
Abstract
BACKGROUND: Existing research suggests that digital games can be used effectively for educational purposes at any level of training. Perioperative nursing educators can use games to complement curricula, in guidance and staff development programs, to foster team collaboration, and to give support to critical thinking in nursing practice because it is a complex environment. OBJECTIVE: To describe the process of developing an educational game to set up surgical instruments on the Mayo stand or back table as a resource to assist the instructor in surgical instrumentation training for students and nursing health professionals in continued education. METHODS: The study was characterized by applied research in production technology. It included the phases of analysis and design, development, and evaluation. The objectives of the educational game were developed through Bloom's taxonomy. Parallel to the physical development of the educational game, a proposed model for the use of digital elements in educational game activities was applied to develop the game content. RESULTS: The development of the game called "Playing with Tweezers" was carried out in 3 phases and was evaluated by 15 participants, comprising students and professional experts in various areas of knowledge such as nursing, information technology, and education. An environment was created with an initial screen, menu buttons containing the rules of the game, and virtual tour modes for learning and assessment. CONCLUSIONS: The "digital" nursing student needs engagement, stimulation, reality, and entertainment, not just readings. "Playing with Tweezers" is an example of educational gaming as an innovative teaching strategy in nursing that encourages the strategy of involving the use of educational games to support theoretical or practical classroom teaching. Thus, the teacher does not work with only 1 type of teaching methodology, but with a combination of different methodologies. In addition, we cannot forget that skill training in an educational game does not replace curricular practice, but helps.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".