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Record W4252659577 · doi:10.1037/e566202009-001

Greater Than the Sum: Systems Thinking in Tobacco Control

2007· dataset· en· W4252659577 on OpenAlexaff
Allan Best, Pamela I. Clark, Scott J. Leischow, William M. K. Trochim

Bibliographic record

VenuePsycEXTRA Dataset · 2007
Typedataset
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsVancouver Coastal Health Research InstituteUniversity of British Columbia
FundersNational Cancer InstituteNational Institutes of Health
KeywordsTobacco controlControl (management)Computer scienceMedicineArtificial intelligenceNursing

Abstract

fetched live from OpenAlex

The evolution of the Tobacco Control Monograph Series underscores its growing importance as a resource for researchers, practitioners, and policy makers in tobacco control as well as in other areas of public health.Lessons learned from tobacco prevention and control can be applied to a variety of public health issues, including physical activity, diet and nutrition, overweight and obesity, and substance abuse.The National Cancer Institute (NCI) is committed to disseminating this cross-cutting knowledge to the widest possible audience so that others can benefit from the experience of the tobacco prevention and control community.By so doing, NCI is increasing the evidence base for effective public health interventions and improving the translation of research to practice and policy.In 1991, NCI published the first monograph in a series designed to address cutting-edge issues and research on tobacco control.That monograph, Strategies to Control Tobacco Use in the United States: A Blueprint for Public Health Action in the 1990's, was visionary in its scope and focus: not only did it acknowledge that tobacco use was a complex problem that demanded new ways of thinking and acting, but it also encouraged expanded exploration of tobacco use issues by the tobacco control community.The three-axis model for the American Stop Smoking Intervention Study for Cancer Prevention (ASSIST), described in Monograph 1, was designed to address the complex interplay of varied target populations, critical channels for intervening (e.g., health care, schools, worksites, and community groups), and intervention types (e.g., mass media, program services, and policy).(See Monograph 16: ASSIST: Shaping the Future of Tobacco Prevention and Control and Monograph 17: Evaluating ASSIST: A Blueprint for Understanding State-level Tobacco Control for more details.)Although it did not adopt the "systems" nomenclature, Monograph 1 laid the foundation for this monograph (Monograph 18), which provides a new and expanded vision of tobacco control as a complex adaptive system.This new model encourages the tobacco control community to (1) collect and use vast arrays of data more effectively; (2) develop and optimize networks to enable the community to more efficiently address varied populations, critical channels for intervention, and intervention types; and (3) support the analysis of complex systems so that more effective strategic decisions are made.Monograph 18 builds on the foundation laid by Monograph 1 by explicitly encouraging (1) the development of informatics infrastructures and collaborative networks, (2) analysis of complex interacting variables, and (3) adoption of new interventions that can speed research to practice (and practice to research).Monograph 18, as the conceptual heir to Monograph 1, provides a new framework for thinking about and acting on the complex relationships among causal factors of public health threats, and it challenges us to consider not just whether we can more effectively use our knowledge of informatics and information management, networks, and complex systems, but whether we will use those essential tools to more rapidly benefit the public's health.

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)0.015
metaresearch head score (Gemma)0.012
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: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0040.024
Scholarly communication0.0130.015
Open science0.0020.005
Research integrity0.0040.010
Insufficient payload (model declined to judge)0.0060.001

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 head0.155
GPT teacher head0.412
Teacher spread0.257 · 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
GenreDataset

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

Citations76
Published2007
Admission routes1
Has abstractyes

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