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Record W3185822561 · doi:10.1111/scd.12634

BeSiDe time to move behavior support in dentistry from an art to a science: A position paper from the BeSiDe (Behavior Support in Dentistry) Group

2021· article· en· W3185822561 on OpenAlexaff
Caoimhin Mac Giolla Phadraig, Tim Newton, Blánaid Daly, Jacobo Limeres Posse, Marie Thérèse Hosey, Carilynne Yarascavitch, Yvonne MacAuley, Heather Buchanan, June Nunn, Ruth Freeman, Chris Stirling, Olive Healy, Koula Asimakopoulou

Bibliographic record

VenueSpecial Care in Dentistry · 2021
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTerminologyPosition (finance)MedicineDentistryDental carePosition paperOrthodontics

Abstract

fetched live from OpenAlex

AIMS: To share the need for agreement in terminology around how people are supported to receive dental care. METHOD: In this position paper, we make the case for a shift in behavior support in dentistry from an art to a science. RESULTS: We outline why we need agreement on the definition of behavior support across dentistry, agreement on underlying theory, aims and values, and why we need agreement on terms for specific techniques. CONCLUSIONS: We share how patients and dental teams can benefit through better science, education and practice of dental behaviour support.

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)0.047
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.953
Threshold uncertainty score0.246

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0470.043
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0060.008
Scholarly communication0.0130.012
Open science0.0030.008
Research integrity0.0170.041
Insufficient payload (model declined to judge)0.0060.003

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 head0.054
GPT teacher head0.357
Teacher spread0.303 · 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.

Study designTheoretical or conceptual
DomainMethods
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

Citations12
Published2021
Admission routes1
Has abstractyes

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