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Record W4399748557 · doi:10.1002/cre2.897

Evaluating the patient sociodemographic factors affecting dental students' clinical communication skills using a three‐perspective approach

2024· article· en· W4399748557 on OpenAlexaff
Siti Mariam Ab Ghani, Puteri Nurul Adila Mohd Khairuddin, Budi Aslinie Md Sabri, Dieter J. Schönwetter, Tong Wah Lim

Bibliographic record

VenueClinical and Experimental Dental Research · 2024
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsUniversity of Manitoba
FundersUniversiti Teknologi MARA
KeywordsPerspective (graphical)Communication skillsAffect (linguistics)MedicineCross-sectional studyPsychologyMedical educationFamily medicinePathology

Abstract

fetched live from OpenAlex

OBJECTIVE: This study aimed to assess undergraduate dental students' communication skills in relation to patient sociodemographic factors using a three-perspective approach; the student, the patient, and the clinical instructor perspective. MATERIALS AND METHODS: A cross-sectional study was conducted using validated modified-communication tools; Patient Communication Assessment Instruments (PCAI), Student Communication Assessment Instruments (SCAI), and Clinical Communication Assessment Instruments (CCAI). Moreover, 176 undergraduate clinical year students were recruited in this study whereby each student was assessed by a clinical instructor, a patient, and self-evaluation. RESULTS: The clinical communication skills domains were not significantly influenced by patient sociodemographic factors, including sex, educational background, and the number of visits (p > .05). However, this study revealed a statistically significant difference in the domain of "caring and respectful" of the SCAI between the low- and middle-income groups. CONCLUSIONS: Overall, most of the patient sociodemographic factors did not affect clinical communication skills. However, patient income groups played a significant role in one of the communication domains.

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)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0050.002
Scholarly communication0.0000.000
Open science0.0010.004
Research integrity0.0010.005
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 head0.687
GPT teacher head0.687
Teacher spread0.000 · 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.

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

Citations3
Published2024
Admission routes1
Has abstractyes

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