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Record W2323305171 · doi:10.3130/aije.77.837

DEVELOPMENT OF REGIONAL ENVIRONMENT ASSESSMENT TOOL FOR HEALTH PROMOTION AND VALIDATION OF THE EFFECTIVENESS

2012· article· en· W2323305171 on OpenAlexaff
Mitsuru DEGUCHI, Toshiharu Ikaga, Shuzo Murakami, Yasuyuki Shiraishi, Tanji Hoshi, Ryuichi Kato, Shun Kawakubo, Shintaro Ando

Bibliographic record

VenueJournal of Environmental Engineering (Transactions of AIJ) · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsDepartment of Environment and Conservation
Fundersnot available
KeywordsNoticeChecklistPromotion (chess)PsychologyHealth promotionHealth careGerontologyEnvironmental healthBusinessApplied psychologyPublic relationsMedicineEconomic growthPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Population aging is accelerating in many developed countries and there are growing concerns about increasing social security costs. Given this background, as stated in the Japanese Growth Strategy, community that can sustain citizens' lifelong health and active living is required. Therefore, this study aims to develop a healthy community checklist based on citizens' consciousness and behavior survey to recognize the condition of their community and notice the risk factor of disease. Based on the answer of the respondents, the score for a community was calculated and verified the effectiveness of this tool. As the result, the relationship between overall community score and citizens' health condition was quantified. Furthermore, the result showed a significant relationship between the community environment and the personal health, even in the case of considering personal life style.

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)0.016
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.026
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.211
Teacher spread0.201 · 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 designBench or experimental
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

Citations6
Published2012
Admission routes1
Has abstractyes

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