Learning While Having Fun: The Use of Video Gaming to Teach Geriatric House Calls to Medical Students
Bibliographic record
Abstract
Although most health professionals perform home visits, there is not a structured method for performing them. In addition, in-training health professionals' exposure to home visits is limited for logistical reasons. A new method for medical students to learn how to perform an effective home visit was developed using an instructional video game. It was expected that students would learn the principles of a home visit using a video game while identifying the usefulness of video gaming (edutainment) in geriatrics education. A video game was created simulating a patient's house that the students were able to explore. Students played against time and distracters while being expected to click on those elements that they considered to be risk factors for falls or harmful for the patient. At the end of the game, the students received feedback on the chosen elements that were right or wrong. Finally, evaluation of the tool was obtained using pre- and posttests and pre- and postexposure feedback surveys. Fifty-six fourth-year medical students used the video game and completed the tests and the feedback surveys. This method showed a high level of engagement that is associated with improvement in knowledge. Additionally, users' feedback indicated that it was an innovative approach to the teaching of health sciences. In summary, this method provides medical students with a fun and structured experience that has an effect not only on their learning, but also on their understanding of the particular needs of the elderly population.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".