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Record W2152489056 · doi:10.5539/gjhs.v7n3p309

The Analysis of Cumulative Influence of Factors of Environment on a State of Health of the Population of Vladimir Region

2015· article· en· W2152489056 on OpenAlexvenueno aff
Тatyana А. Trifonova, Leonid Shirkin

Bibliographic record

VenueGlobal Journal of Health Science · 2015
Typearticle
Languageen
FieldMedicine
TopicHealthcare Systems and Public Health
Canadian institutionsnot available
Fundersnot available
KeywordsState (computer science)PopulationState of healthPsychologyDemographyEnvironmental healthGerontologyGeographyMedicineSociologyMathematicsPhysicsThermodynamics

Abstract

fetched live from OpenAlex

There was investigated the contribution of factors of environment to formation of health for adult population on indicators of mid-annual rates of growth/decrease of disease of system of blood circulation and of some interfaced nosology on an example of the population of Vladimir region. The differential criterion of primary disease of system of blood circulation is considered as an indicator, integrally reflecting degree of adaptation to environment conditions on population and suitable for construction short-term prognostic estimations. It is shown that business factors or the factors of a standard of living characterized by economic indicators, are leading risk factors in disease of system of blood circulation in Vladimir region which contribution is estimated by size of 38%. With use of regressive equations were received look-ahead estimations of annual rates of primary disease of system of blood circulation. In the regional centre Vladimir was observed more intense situation on rates of disease of system of blood circulation, than in Vladimir region.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.075
GPT teacher head0.398
Teacher spread0.322 · 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 designObservational
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

Citations1
Published2015
Admission routes1
Has abstractyes

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