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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 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)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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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