The Community-Based Learning Model via Game Simulation to Promote Community Public Health Diagnosis Skills
Bibliographic record
Abstract
The community-based learning model via game simulation to promote community public health diagnosis skills, or CBL model via game simulation, is a research tool that was devised based on the concepts of public health diagnosis using the seven community tools (geo-social mapping, genogram, community organization chart, local health system, community calendar, local history, and life story) combined with the simulation game-based learning. The learning of this style encourages learners to learn and conduct activities in virtual environment of metaverse. In addition, it is believed that this will help promote learners’ community public health diagnosis skills and systematic thinking skills as well. This study is intended to design the CBL model via game simulation as a guideline to further develop the CBL system via game simulation with self-directed learning to promote community public health diagnosis skills. The sample group are nine experts who are experience in design of instruction models. The tools employed in this research consist of (1) the CBL model via game simulation, and (2) the evaluation form on the suitability of the CBL model via game simulation. This study shows that (1) the overall elements of the CBL model via game simulation is at highest level, and (2) the overall suitability of the elements of the CBL model via game simulation is at highest level as well.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".