A low-fidelity serious game for medical-based cultural competence education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Research has shown that the quality of care is compromised when healthcare providers respond inappropriately to patient language and cultural factors. However, research indicates that medical education is not keeping pace with the changing composition of the patient population in culturally diverse societies such as Canada and the United States, and many healthcare providers do not possess the attitudes or skills required to be effective within a culturally diverse healthcare setting. Here, we present Fydlyty, a web-based, low-fidelity serious game for medical-based cultural competence education. Fydlyty includes both a scenario and dialogue editor providing the ability to develop conversations, interpret responses, and respond to questions/answers from the game player. These responses are based on predefined cultural characteristics of the virtual patient and on different moods that the virtual patient may express depending on the situation. The results of a usability experiment conducted with medical professionals and trainees revealed that the game is easy to use, intuitive, and engaging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it