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Record W2010811374 · doi:10.1097/jom.0b013e3181d46f0b

Second-Year Results of an Obesity Prevention Program at The Dow Chemical Company

2010· article· en· W2010811374 on OpenAlexaff
Ron Z. Goetzel, Enid Chung Roemer, Xiaofei Pei, Meghan E. Short, Maryam Tabrizi, Mark G. Wilson, David M. DeJoy, Beth A. Craun, Karen J. Tully, J.M. White, Catherine M. Baase

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2010
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsThomson Reuters (Canada)
FundersNational Heart, Lung, and Blood Institute
KeywordsBody mass indexConfoundingPsychological interventionObesityMedicinePropensity score matchingWeight managementIntervention (counseling)Physical therapyEnvironmental healthGerontologyOverweightDemographyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Evaluate innovative, evidence-based approaches to organizational/supportive environmental interventions aimed at reducing the prevalence of obesity among Dow employees after 2 years of implementation. METHODS: A quasi-experimental study design compared outcomes for two levels of intervention intensity with a control group. Propensity scores were used to weight baseline differences between intervention and control subjects. Difference-in-differences methods and multilevel modeling were used to control for individual and site-level confounders. RESULTS: Intervention participants maintained their weight and body mass index, whereas control participants gained 1.3 pounds and increased their body mass index values by 0.2 over 2 years. Significant differences in blood pressure and cholesterol values were observed when comparing intervention employees with controls. At higher intensity sites, improvements were more pronounced. CONCLUSIONS: Environmental interventions at the workplace can support weight management and risk reduction after 2 years.

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)0.001
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 score0.123
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.383
Teacher spread0.356 · 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

Citations76
Published2010
Admission routes1
Has abstractyes

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