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Record W4295487995 · doi:10.2196/36079

Preliminary User Evaluation of a New Dental Technology Virtual Simulation System: Development and Validation Study

2022· article· en· W4295487995 on OpenAlexvenueno aff
MengWei Pang, Xiaohan Zhao, Daiyu Lu, Yihan Dong, Lin Jiang, Jie Li, Ping Ji

Bibliographic record

VenueJMIR Serious Games · 2022
Typearticle
Languageen
FieldDentistry
TopicDental Research and COVID-19
Canadian institutionsnot available
FundersChongqing Medical UniversityNational Natural Science Foundation of China
KeywordsComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: With the advancements in the dental health care industry, the demand for dental technicians has increased. Dental technicians should be thoroughly assessed and trained in practical skills and pass professional certification examinations to ensure that they are competent to work closely with dentists. Unfortunately, such training courses and tests are in short supply worldwide. The use of virtual simulation technology can help solve these problems. OBJECTIVE: This study presents a new strategic framework design for a certified dental technician practical examination called as the certified Objective Manipulative Skill Examination of Dental Technicians (OMEDT), which is based on the Objective Structured Clinical Examination (OSCE). We present the development and validation of the OMEDT system, a new virtual simulated training system, to meet the demands of the OMEDT framework. The combination of OMEDT and the OMEDT system can solve the complex problems encountered in the certified dental technician practical examination with excellent efficiency, high quality, and low cost. METHODS: The OMEDT framework design was constructed according to the OSCE guide and the Chinese vocational skill standards for dental technicians. To develop the OMEDT system, we organized a new framework based on the virtual learning network platform, the haptic feedback system, and the real-time dental training and evaluation system. The effectiveness evaluation of the OMEDT system was divided into 2 phases: in the first phase, 36 students were recruited to use the test module to finish the task and their performance data were collected and analyzed; and in the second phase, a questionnaire was administered to 30 students who used the system for their studies and graduation exams. RESULTS: The OMEDT and the corresponding skill training virtual simulation OMEDT system were developed, and preliminary user evaluation was performed to assess their effectiveness and usefulness. The OMEDT system was found to improve students' practical skills by training with the evaluation results. In addition, several key research topics were explored, including the effects of positive feedback of the knowledge of results on the improvement of the students' skill level and the common sense transformation of educators in the virtual simulation technology environment. CONCLUSIONS: The development of OMEDT and the OMEDT system has been completed and their effectiveness has been verified.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.578
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.041
GPT teacher head0.370
Teacher spread0.329 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2022
Admission routes1
Has abstractyes

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