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Record W4407287531 · doi:10.5539/jel.v14n3p230

The Community-Based Learning Model via Game Simulation to Promote Community Public Health Diagnosis Skills

2025· article· en· W4407287531 on OpenAlex

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Education and Learning · 2025
Typearticle
Languageen
FieldComputer Science
TopicEdcuational Technology Systems
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyMathematics educationCommunity healthPublic healthMedicine

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.617
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.050
GPT teacher head0.363
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it