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Record W2982107103 · doi:10.1111/eje.12476

Attitudes of dental students in a Canadian university towards communication skills learning

2019· article· en· W2982107103 on OpenAlexafffundabout
Caitlyn Ayn, Lynne Robinson, Debora Matthews, Cynthia Andrews

Bibliographic record

VenueEuropean Journal Of Dental Education · 2019
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsDalhousie University
FundersFaculty of Graduate Studies, Dalhousie UniversityNova Scotia Health Research Foundation
KeywordsCurriculumMedical educationCommunication skillsScale (ratio)Ethnic groupPerceptionPromotion (chess)PsychologyMedicineOral healthPedagogyFamily medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Strong dentist communication skills (CS) are necessary for patient-centred care and oral health promotion. CS are imparted through the dental education experience, which can be optimised in part by incorporating student perceptions and needs into curricular development. The current study assessed student attitudes towards communication skills learning (CSL) in a Canadian university. METHODS: A 20-item questionnaire adapted from the Dental Communication Skills Attitude Scale and qualitative survey questions were completed by students (n = 124). RESULTS: Questionnaire findings indicate that attitudes towards CSL are generally favourable, with significant variation based on year of study, gender and ethnicity. Students understood the importance of CS for dental practice and patient-centred care. Whilst they appreciated the value of CSL, students described that challenges such as demanding programme schedules would preclude the utility of more formalised CSL activities. CONCLUSION: These findings may be useful for institutions seeking to implement or refine a CSL curriculum.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.203
Threshold uncertainty score0.409

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.002
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.056
GPT teacher head0.396
Teacher spread0.340 · 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 source (direct Gemma or distilled Codex), 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

Citations14
Published2019
Admission routes3
Has abstractyes

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