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The Virtual Child: Evaluation of an Internet‐Based Pediatric Behavior Management Simulation

2007· article· en· W2098403087 on OpenAlexaff
James R. Boynton, Thomas G. Green, Lynn Johnson, S M Hashim Nainar, Lloyd H. Straffon

Bibliographic record

VenueJournal of Dental Education · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBehavior managementCurriculumThe InternetMedical educationClass (philosophy)Test (biology)Computer sciencePsychologyMedicineMultimediaPedagogyWorld Wide Web

Abstract

fetched live from OpenAlex

This article describes an Internet-based instructional tool designed to give predoctoral dental students a virtual simulation of clinical pediatric dentistry to develop their pediatric behavior management knowledge. Effectiveness of this tool was evaluated using two consecutive classes of junior dental students. The control group was exposed to the traditional behavior management curriculum (two lectures) in a spring term course. The next class of dental students was exposed to the two lectures and, in addition, completed the behavior management simulation during the following spring term. Both groups completed a two-part examination (objective section=18 questions; open-ended section=responses to a clinical situation) designed to test their behavior management knowledge. The simulation group performed significantly better in both parts of the examination (objective section: p=.028; open-ended section: p=.012). The simulation was evaluated by students and perceived by most to be an effective addition to the curriculum. It was concluded that the experimental behavior management simulation, when added to the traditional lecture curriculum, improved pediatric behavior management knowledge in predoctoral dental students.

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)0.006
metaresearch head score (Gemma)0.001
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 score0.921
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
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 head0.071
GPT teacher head0.478
Teacher spread0.407 · 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

Citations30
Published2007
Admission routes1
Has abstractyes

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