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Record W7128267485 · doi:10.58532/nbennurcihpsw8

CULTURAL SENSITIVITY AND RESPONSIVENESS IN DENTAL EDUCATION: SUGGESTED STRATEGIES FOR ENHANCING DIVERSITY AND INCLUSIVENESS

2025· book-chapter· W7128267485 on OpenAlexaboutno aff
Prof. Dr. Ramesh Kumaresan, Prof. Dr. M. Shivasakthy Manivasakan

Bibliographic record

Venuenot available
Typebook-chapter
Language
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsnot available
Fundersnot available
KeywordsAccreditationCurriculumCultural competenceCultural diversityMulticulturalismCompetence (human resources)Cultural sensitivityHealth care

Abstract

fetched live from OpenAlex

Cultural sensitivity and responsiveness are essential components of modern dental education, ensuring that future practitioners are equipped to provide inclusive, patientcentered care in increasingly diverse communities. This chapter explores the current landscape of cultural competency in dental curricula across various international contexts, including the United States, Canada, the United Kingdom, Australia, India, and Malaysia. It critically examines institutional practices, accreditation standards, and curriculum structures, identifying strengths and persistent gaps. Emphasis is placed on the importance of faculty development, inclusive teaching environments, service-learning, and the use of standardized assessment tools. Drawing on evidence-based strategies and educational frameworks, the chapter proposes actionable recommendations to embed cultural competence into all levels of dental education. These strategies aim to foster diversity, promote health equity, and prepare graduates to effectively address the oral health needs of multicultural populations in a globally connected healthcare landscape.

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)0.005
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.006
Scholarly communication0.0070.005
Open science0.0020.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.035
GPT teacher head0.343
Teacher spread0.309 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2025
Admission routes1
Has abstractyes

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