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How Do U.S. and Canadian Dental Schools Teach Interpersonal Communication Skills?

2002· article· en· W2115797691 on OpenAlexaboutno aff
Toshiko Yoshida, Peter Milgrom, Susan E. Coldwell

Bibliographic record

VenueJournal of Dental Education · 2002
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsInterpersonal communicationInterviewMedical educationAccreditationSyllabusPsychologyCommunication skillsPedagogyMedicineSociology

Abstract

fetched live from OpenAlex

The status of instruction in interpersonal communication was surveyed in forty U.S and Canadian dental schools. Key faculty members were identified, and syllabi and course descriptions were collected and content-analyzed. The following findings were obtained for responding schools: 1) only one-third of schools had courses specifically focusing on interpersonal communication; 2) more than half of the schools offered these types of courses only during the first two years; 3) the most common topics were communication skills, patient interviewing, and patient education/consultation; 4) the most frequently used method of teaching was lectures; active practice was used less often; 5) written examination was the primary instructional evaluation tool, whereas more sophisticated performance-oriented assessments were used less often; and 6) about half of the teachers did not have a D.D.S. degree; those not dentists were primarily psychologists. At least eight of the forty dental schools surveyed do not appear to meet the accreditation guidelines for predoctoral programs in this area of instruction. Some could not identify a faculty member responsible for such instruction. Schools offering more extensive instruction were more likely to offer active rather than passive teaching and use more sophisticated student evaluation strategies. This research suggests a need for reevaluation of teaching in this subject area.

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 score0.508
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.071
GPT teacher head0.388
Teacher spread0.317 · 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

Citations121
Published2002
Admission routes1
Has abstractyes

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