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Efficacy of a Step‐by‐Step Carving Technique for Dental Students

2013· article· en· W2118556331 on OpenAlexaff
Alan J Kilistoff, Louis Mackenzie, Marcel D’Eon, Krista Trinder

Bibliographic record

VenueJournal of Dental Education · 2013
Typearticle
Languageen
FieldDentistry
TopicDental Research and COVID-19
Canadian institutionsUniversity of SaskatchewanUniversity of Alberta
Fundersnot available
KeywordsCarvingDentistryTest (biology)Dental educationMedicineOrthodonticsMathematics educationMedical educationPsychologyEngineering

Abstract

fetched live from OpenAlex

This study demonstrates the effectiveness of a step-by-step carving technique that is quickly and efficiently mastered by dental students. Thirty-six final-year dental students volunteered to participate in this study. The students were given pre-prepared lower right first molar simulation teeth that had the occlusal half replaced in carving wax. The study was conducted in three time phases: pre-test (Time 1), participative learning (Time 2), and post-test (Time 3). The pre-test had the students carve the wax with no instruction. Instruction and demonstration of the technique were given at Time 2, and the post-test had the students carve the tooth again with no guidance but with training. A statistically significant increase with a nearly medium effect size was found from Time 1 to Time 2. A statistically significant increase with a medium effect size was found when comparing Time 2 to Time 3. A statistically significant increase with a large effect size was found when comparing Time 1 to Time 3. This technique has proved to be an effective method of simultaneously teaching a large cohort of predoctoral dental students. The technique is consistent with constructivist learning theory.

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)0.000
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 score0.458
Threshold uncertainty score0.640

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.394
Teacher spread0.377 · 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

Citations29
Published2013
Admission routes1
Has abstractyes

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