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Record W2092136221 · doi:10.2147/rmhp.s7528

An integrated approach to preventing cardiovascular disease: community-based approaches, health system initiatives, and public health policy

2010· article· en· W2092136221 on OpenAlexaff
Janusz Kaczorowski

Bibliographic record

VenueRisk Management and Healthcare Policy · 2010
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversity of British ColumbiaChild and Family Research Institute
Fundersnot available
KeywordsPublic healthDiseaseMedicineHealth policyEnvironmental healthNursingPathology

Abstract

fetched live from OpenAlex

Cardiovascular disease (CVD) is largely the product of interactions among modifiable risk factors that are common in developed nations and increasingly of concern in developing countries. Hypertension is an important precursor to the development of CVD, and although detection and treatment rates have improved in recent years in some jurisdictions, effective strategies and policies supporting a shift in distribution of risk factors at the population level remain paramount. Challenges in managing cardiovascular health more effectively include factors at the patient, provider, and system level. Strategies to reduce hypertension and CVD should be population based, incorporate multilevel, multicomponent, and socioenvironmental approaches, and integrate community resources with public health and clinical care. There is an urgent need to improve monitoring and management of risk factors through community-wide, primary care-linked initiatives, increase the evidence base for community-based prevention strategies, further develop and evaluate promising program components, and develop new approaches to support healthy lifestyle behaviors in diverse age, socioeconomic, and ethnocultural groups. Policy and system changes are critical to reduce risk in populations, including legislation and public education to reduce dietary sodium and trans-fatty acids, food pricing policies, and changes to health care delivery systems to explicitly support prevention and management of CVD.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.769
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.071
GPT teacher head0.333
Teacher spread0.262 · 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.

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

Citations36
Published2010
Admission routes1
Has abstractyes

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