MétaCan
Menu
Back to cohort
Record W4408417667 · doi:10.3389/froh.2025.1547335

Integrating design thinking into dental education

2025· review· en· W4408417667 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers in Oral Health · 2025
Typereview
Languageen
FieldComputer Science
TopicPersona Design and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsDesign thinkingCritical thinkingActive learning (machine learning)Collaborative learningConstructiveProcess (computing)Constructivist teaching methodsPsychologyKnowledge managementComputer scienceMathematics educationPedagogyEngineering ethicsTeaching methodEngineeringHuman–computer interaction

Abstract

fetched live from OpenAlex

Design thinking is a human-centred, iterative process that aims to develop innovative solutions tailored to user needs. This article examines the groundwork and incorporation of design thinking in healthcare and medical education, highlighting its potential benefits in dental education, including enhancements in learner-centred approaches, faculty development, interprofessional collaboration, and person-centred care. Design thinking methods foster learner engagement, aligning with cognitive and constructivist learning theories. Active engagement and discourse among learners create meaningful learning experiences, benefiting from a "learning by doing" approach. Further, design thinking processes ensure critical thinking and collaborative learning, supporting active engagement with prior knowledge and constructive feedback skills. Thus, applying design thinking in dental education could deepen learners' understanding with improved problem-solving skills, ultimately leading to effective learning outcomes.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.946
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.055
GPT teacher head0.389
Teacher spread0.334 · 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