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Record W4404864037 · doi:10.1177/10105395241300767

The 8020 Campaign in Japan: A Policy Analysis

2024· article· en· W4404864037 on OpenAlexaboutno aff
Sachiko Takehara, Raksanan Karawekpanyawong, Hikaru Okubo, Tin Zar Tun, Aulia Ramadhani, Fania Chairunisa, Azusa Tanaka, Tippanart Vichayanrat, Clive Wright, Hiroshi Ogawa

Bibliographic record

VenueAsia Pacific Journal of Public Health · 2024
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsnot available
Funders8020 Promotion Foundation
KeywordsCharterContext (archaeology)Health promotionPromotion (chess)Data collectionQualitative researchOral healthQualitative propertyOral historyPolitical scienceMedicinePublic relationsPublic healthSociologyNursingFamily medicineGeographyPoliticsComputer scienceLawSocial science

Abstract

fetched live from OpenAlex

Japan initiated a nationwide oral health promotion movement called the 8020 Campaign in 1989, which promoted oral health via a positive message: let's keep at least 20 teeth when we reach the age of 80. This study aimed to understand Japan's 8020 Campaign in terms of its content, actors, processes, and context. This study used qualitative data collection methods consisting of a literature review and Key Informant Interviews (KIIs). The data were analyzed using the health policy triangle framework. The results showed that the 8020 Campaign promoted oral health for individuals of all ages using a life-course approach and followed the Ottawa Charter framework for its core activities. The major facilitating factors suggested were stable financial support, initiatives led by local governments, the enactment of oral health laws and ordinances, the establishment of the 8020 Promotion Foundation, and increased attention focused on preventive approaches to oral health in Japan.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.033
GPT teacher head0.359
Teacher spread0.326 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations5
Published2024
Admission routes1
Has abstractyes

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