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Record W2098925346 · doi:10.1080/10807030701655798

An Integrated Framework for Risk Management and Population Health

2007· article· en· W2098925346 on OpenAlexafffund
Daniel Krewski, Victoria Hogan, Michelle C. Turner, Patricia L. Zeman, Ian McDowell, Nancy Edwards, Joseph Losos

Bibliographic record

VenueHuman and Ecological Risk Assessment An International Journal · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsInstitute of Population and Public HealthUniversity of Ottawa
FundersHealth CanadaNational Academy of SciencesNatural Sciences and Engineering Research Council of CanadaCanadian Institute for Advanced Research
KeywordsRisk analysis (engineering)Risk managementPopulation healthPopulationBusinessEnvironmental planningEnvironmental healthEnvironmental resource managementMedicineGeographyEnvironmental science

Abstract

fetched live from OpenAlex

ABSTRACT The traditional medical model of health and health policy development has focused on individuals and the role of medical care in preventing and treating disease and injury. Recent attention to health inequities and social determinants of health has raised the profile of population heath and evidence-based strategies for improving the health of whole populations. At the same time, risk science has emerged as an important new discipline for the assessment and management of risks to health. This article reviews historical developments in the fields of risk management and population health and proposes a joint population health risk management framework that integrates the key elements of both fields. Applying this integrated approach to managing population health risks will facilitate the development of evidence-based health policy. It will encourage a more systematic and comprehensive evaluation of population health issues and promote the use of a broader suite of interventions to reduce health risks and enhance population health status.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.068
GPT teacher head0.437
Teacher spread0.369 · 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

Citations60
Published2007
Admission routes2
Has abstractyes

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