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Record W2925245771 · doi:10.1186/s13584-019-0302-z

Is the oral health reform in Israel optimally distributed? - A commentary

2019· letter· en· W2925245771 on OpenAlexaboutno aff
Harold D. Sgan‐Cohen, Guy Tobias, Avraham Zini

Bibliographic record

VenueIsrael Journal of Health Policy Research · 2019
Typeletter
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsnot available
Fundersnot available
KeywordsCharterPublic healthMedicineHealth policyHealth services researchGovernment (linguistics)PopulationSocial policyGerontologyEconomic growthPolitical scienceFamily medicineEnvironmental healthLawNursing

Abstract

fetched live from OpenAlex

A traditional and ethical principle recognizes a country's primary general welfare responsibility to the young and the old. However, the middle, adult, age group cannot and should not be disregarded. The current dental component of the National Health Insurance Law (NHIL), in Israel, only includes children and the elderly. The present commentary focuses on the large group of adults, age 19-74, which are currently excluded.The cumulative incidence of disease increases over the lifetime of a person. We believe that a NHIL commitment with a major age gap in coverage is unacceptable. The recent manuscript, published by Natapov et al., in this journal, has documented the overall dental health of the older Israeli population, with emphasis on nutritional aspects. This contribution to the literature is commendable. However, we aim to follow in the steps of the Alma Ata Declaration and Ottawa Charter of the World Health Organization (WHO) and to clarify that the government's responsibility should cover all residents regardless of their age. In addition, a dental health epidemiological data base, currently nonexistent for adults, is called for.

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)0.012
metaresearch head score (Gemma)0.039
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 score0.073
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.039
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0090.010
Scholarly communication0.0080.006
Open science0.0040.004
Research integrity0.0730.037
Insufficient payload (model declined to judge)0.0080.002

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 head0.176
GPT teacher head0.521
Teacher spread0.345 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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