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Teaching Methods and Surgical Training in North American Graduate Periodontics Programs: Exploring the Landscape

2010· article· en· W2113382053 on OpenAlexaffabout
Edmond Ghiabi, K. Lynn Taylor

Bibliographic record

VenueJournal of Dental Education · 2010
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPeriodontologyCurriculumMedical educationMedicineTest (biology)Faculty developmentSurgical proceduresProfessional developmentDentistryPsychologySurgeryPedagogy

Abstract

fetched live from OpenAlex

This project aimed at documenting the surgical training curricula offered by North American graduate periodontics programs. A survey consisting of questions on teaching methods employed and the content of the surgical training program was mailed to directors of all fifty-eight graduate periodontics programs in Canada and the United States. The chi-square test was used to assess whether the residents' clinical experience was significantly (P<0.05) influenced by having a) a structured preclinical program or b) another dental residency program in the institution. Thirty-four programs (59 percent) responded to the survey. Twenty-six programs (76 percent of respondents) reported offering a structured preclinical component. Traditional teaching methods such as slides, live demonstration, DVD/CD, and animal cadavers were the most common teaching methods used, whereas online courses, computer simulation, and various surgical mannequins were least commonly used. The most commonly performed surgical procedures were conventional flaps, periodontal plastic procedures, hard tissue grafts, and implants. Furthermore, residents in programs offering a structured preclinical component performed significantly more procedures (P=0.012) using lasers than those in programs not offering a structured preclinical program. Devising new and innovative teaching methods is a clear avenue for future development in North American graduate periodontics programs.

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 score0.708
Threshold uncertainty score0.295

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 integrity0.0000.001
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 head0.090
GPT teacher head0.407
Teacher spread0.316 · 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
Published2010
Admission routes2
Has abstractyes

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