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Record W2918819596 · doi:10.1097/jom.0000000000000868

A Prospective Programmatic Cost Analysis of Fuel Your Life

2016· article· en· W2918819596 on OpenAlexaff
Justin B. Ingels, Rebecca Walcott, Mark G. Wilson, Phaedra S. Corso, Heather M. Padilla, Heather Zuercher, David M. DeJoy, Robert J. Vandenberg

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsWorkplace Health, Safety and Compensation Commission
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsPhoneModalitiesModality (human–computer interaction)Activity-based costingTransparency (behavior)Treatment modalityOperations managementCost–benefit analysisCost analysisMedicineMedical emergencyBusinessComputer scienceEngineeringOperations researchSurgeryAccountingComputer securityPolitical science

Abstract

fetched live from OpenAlex

OBJECTIVE: An accounting of the resources necessary for implementation of efficacious programs is important for economic evaluations and dissemination. METHODS: A programmatic costs analysis was conducted prospectively in conjunction with an efficacy trial of Fuel Your Life (FYL), a worksite translation of the Diabetes Prevention Program. FYL was implemented through three different modalities, Group, Phone, and Self-study, using a micro-costing approach from both the employer and societal perspectives. RESULTS: The Phone modality was the most costly at $354.6 per participant, compared with $154.6 and $75.5 for the Group and Self-study modalities, respectively. With the inclusion of participant-related costs, the Phone modality was still more expensive than the Group modality but with a smaller incremental difference ($461.4 vs $368.1). CONCLUSIONS: This level of cost-related detail for a preventive intervention is rare, and our analysis can aid in the transparency of future economic evaluations.

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)0.014
metaresearch head score (Gemma)0.037
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.037
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.277
GPT teacher head0.421
Teacher spread0.144 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

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