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Record W2766849954 · doi:10.1177/2380084417738001

Time to Develop Social Dentistry

2017· article· en· W2766849954 on OpenAlexafffund
Christophe Bedos, Nareg Apelian, Jean‐Noël Vergnes

Bibliographic record

VenueJDR Clinical & Translational Research · 2017
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsUniversité de MontréalMcGill University
FundersBiodiversity-Based Economy Development OfficeMcGill UniversityWorld Health Organization
KeywordsHealth careHealth professionalsSocial careStatement (logic)Oral healthDental careOral health carePsychologyMedical educationMedicinePublic relationsDentistryNursingPolitical science

Abstract

fetched live from OpenAlex

Knowledge Transfer Statement: We are calling researchers, educators, and dental professionals to be at the forefront of actions addressing social determinants of health. We indeed argue that 1) it is the dentists' and other oral health care professionals' role to tackle social determinants of health and 2) as researchers and educators, we need to help clinicians in this endeavor and lead the development of a "social dentistry" movement.

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)0.013
metaresearch head score (Gemma)0.045
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.217
Threshold uncertainty score0.727

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.045
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0080.003
Scholarly communication0.0090.008
Open science0.0030.017
Research integrity0.0120.018
Insufficient payload (model declined to judge)0.2170.099

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 head0.825
GPT teacher head0.732
Teacher spread0.093 · 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

Citations18
Published2017
Admission routes2
Has abstractyes

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