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Record W4231281749 · doi:10.1108/oxan-db197769

Chronic disease will constrain China's economic growth

2015· other· en· W4231281749 on OpenAlexaboutno aff

Bibliographic record

VenueEmerald expert briefings · 2015
Typeother
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsChinaGovernment (linguistics)Quarter (Canadian coin)Investment (military)DiseaseChronic diseaseHealth careEconomic growthDisease burdenBusinessPopulationBurden of diseaseEnvironmental healthMedicineGeographyEconomicsPolitical scienceFamily medicine

Abstract

fetched live from OpenAlex

Subject Chronic diseases and their impact on China's economic prospects. Significance Non-communicable diseases (NCDs) are now China's number-one health threat, contributing more than 80% of the country's 10.3 million annual deaths and nearly 70% of its total disease burden. With less than one-fifth of the world's population, China already suffers more than one-quarter of its cancer deaths. NCDs have been a priority for China since the 12th Five-year Plan was launched in 2011. However, the rate at which the chronic disease burden is growing is alarming. Impacts Greater investment in primary healthcare and social care is likely under the 13th Five-year Plan (2016-20). Business opportunities will expand in the healthcare, pharmaceuticals and health insurance sectors. As the challenge of chronic disease grows, the government is likely to extend market access to previously regulated sectors.

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)0.002
metaresearch head score (Gemma)0.004
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: none
Teacher disagreement score0.087
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0480.007

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 head0.019
GPT teacher head0.278
Teacher spread0.259 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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