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Record W2006579925 · doi:10.1097/jom.0b013e3182717cd4

Workplace-Based Participatory Approach to Weight Loss for Correctional Employees

2013· article· en· W2006579925 on OpenAlexaff
Lindsay Ferraro, Pouran D. Faghri, Robert A. Henning, Martin Cherniack

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2013
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsWorkplace Health, Safety and Compensation Commission
FundersNational Institute for Occupational Safety and Health
KeywordsWaistBody mass indexWeight lossMedicineCircumferenceConfidence intervalGerontologyIntervention (counseling)Physical therapyBody weightCitizen journalismIndex (typography)DemographyObesityNursingInternal medicineMathematicsSociology

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the effectiveness of a participatory approach using an employee design team for a 12-week weight-loss program with an 8-week follow-up. METHODS: Twenty-four employees with mean [standard error (SE)] for weight 233.24 lb [8.16], body mass index 33.29 kg/cm [0.82], and age 42.7 years [1.5] participated in the study, among whom 75% were men and 25% women. RESULTS: Significant reductions in weight, body mass index, and waist circumference (among men) were observed before and after intervention (P < 0.05). About 73% and 68% of the variation in weight change (P < 0.01) and waist circumference (P < 0.01), respectively, were explained by Nutrition Knowledge and Exercise Confidence scores after controlling for gender and age. CONCLUSIONS: A participatory program with employee involvement resulted in positive outcomes. Increasing participants' knowledge and providing skills to manage their weight seem to change their attitudes, resulting in better outcomes.

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 score0.061
Threshold uncertainty score0.999

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.116
GPT teacher head0.420
Teacher spread0.304 · 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

Citations26
Published2013
Admission routes1
Has abstractyes

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