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Substance Use and Dependence Education in Predoctoral Dental Curricula: Results of a Survey of U.S. and Canadian Dental Schools

2011· article· en· W2114379725 on OpenAlexaboutno aff
Kathryn N. Huggett, Gary H. Westerman, Eugene J. Barone, Amanda Lofgreen

Bibliographic record

VenueJournal of Dental Education · 2011
Typearticle
Languageen
FieldDentistry
TopicDental Research and COVID-19
Canadian institutionsnot available
FundersNational Institute on Drug AbuseNational Institutes of Health
KeywordsCurriculumSubstance abuseMedical prescriptionDrug educationMedicineFamily medicineMedical educationAlcohol abuseDental educationSubstance usePsychologyNursingPsychiatryPedagogy

Abstract

fetched live from OpenAlex

The purpose of this study was to obtain information about education in substance use and dependence that appears in the predoctoral curricula of U.S. and Canadian dental schools. Sixty-eight deans were sent a twenty-item survey requesting information about when in the curriculum these subjects were taught, what instructional methods were used, and whether behavior change instruction was included to address these issues in clinical interactions. The survey had an 81 percent response rate. The topics of alcohol use and dependence, tobacco use and dependence, and prescription drug misuse and abuse were reported in over 90 percent (N=55) of responding schools' predoctoral curricula. The topic of other substance use and dependence was reported in only 72.7 percent (N=40) of these schools. The primary instructional method reported was the use of lecture. Less frequently used methods included small-group instruction, instruction in school-based clinic, community-based extramural settings, and independent study. As future health professionals, dental students are an important source for patients concerning substance use, abuse, and treatment. Our investigation confirmed that alcohol, tobacco, and prescription drug abuse is addressed widely in predoctoral dental curricula, but other substance use and dependence are less frequently addressed.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score0.643

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.062
GPT teacher head0.356
Teacher spread0.294 · 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

Citations20
Published2011
Admission routes1
Has abstractyes

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