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Record W4200129143 · doi:10.1002/jdd.12861

The dental “box of horrors” clinical practice game: A pilot project

2021· article· en· W4200129143 on OpenAlexaboutno aff
Leslie Borsa, Paul Tramini, Laurence Lupi

Bibliographic record

VenueJournal of Dental Education · 2021
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsnot available
Fundersnot available
KeywordsDentistryPsychologyDental educationDental practiceMedical educationMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: The dental department of the University Hospital of Nice has set the "box of horrors," an innovative concept inspired by the "room of horrors," created in 2006 in Canada, and utilized since 2011 in France. PURPOSE: The aim was to assess the impact and perceived value of this clinical practice game utilized by fourth-year dental students. METHODS: This pilot study following a cross-sectional pre- and posttest research design was used to assess students' change in performance. The experimental group was divided into 12 students teams (n = 50). A questionnaire was completed before they entered the box; they had then to find out 10 errors hidden in the box in a set time. A debriefing was held immediately after. The control group answered the same questions but did not follow the course inside the box. The percentages of correct answers were compared between the two groups with a Mann-Whitney test, and the scores per student were analyzed with a mixed effects ordinal multiple logistic regression. Finally, a satisfaction questionnaire was proposed. RESULTS: After the course, the students from the experimental group performed 94% correct answers, while those from the control group showed 78% (significant difference). The outcome of the mixed effect multiple regression showed a significant group effect (p = 0.0001) and gender effect (p = 0.001). CONCLUSION: Clinical games, although complex to implement, are interesting and rewarding tools. The adaptation of the tool to the dental sector appeared to be feasible.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.064
GPT teacher head0.475
Teacher spread0.411 · 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 designNot applicable
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

Citations6
Published2021
Admission routes1
Has abstractyes

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