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Record W3200156181 · doi:10.1111/eje.12720

Behavioural change as a theme that integrates behavioural sciences in dental education

2021· article· en· W3200156181 on OpenAlexaff
Arnaldo Perez, Jacqueline Green, Geoff D.C. Ball, Maryam Amin, Sharon M. Compton, Steven Patterson

Bibliographic record

VenueEuropean Journal Of Dental Education · 2021
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCurriculumTheme (computing)Behavioural sciencesContext (archaeology)Relevance (law)Medical educationDental educationPsychologyBehavior changeIntervention (counseling)MedicinePedagogyPsychotherapistSocial psychologyComputer sciencePsychiatryPolitical science

Abstract

fetched live from OpenAlex

The behavioural sciences curriculum in dental education is often fragmented and its clinical relevance is not always apparent to learners. Curriculum integration is vital to understand behavioural subjects that are interrelated but frequently delivered as separate issues in dental programmes. In this commentary, we discuss behavioural change as a curricular theme that can integrate behavioural sciences in dental programmes. Specifically, we discuss behavioural change in the context of dental education guidelines and describe four general phases of behavioural change (defining the target behaviour, identifying the behavioural determinants, applying appropriate behavioural change techniques and evaluating the behavioural intervention) to make the case for content that can be covered within this curricular theme, including its sequencing. This commentary is part of ongoing efforts to improve the behavioural sciences curriculum in dental education in order to ensure that dental students develop the behavioural competencies required for entry-level general dentists.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.295
GPT teacher head0.400
Teacher spread0.105 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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