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Record W2055781058 · doi:10.1097/phh.0b013e31829d7b7c

Reducing Sodium Across the Board

2013· article· en· W2055781058 on OpenAlexaboutno aff
June Schuldt, Jessica Lee Levings, Jennifer L. Kahn-Marshall, G.S. Hunt, Kristy Mugavero, Janelle P. Gunn

Bibliographic record

VenueJournal of Public Health Management and Practice · 2013
Typearticle
Languageen
FieldNursing
TopicSodium Intake and Health
Canadian institutionsnot available
FundersNational Institutes of Health
KeywordsProduct (mathematics)Quarter (Canadian coin)BusinessPublic healthProduct categoryMarketingMedicineEnvironmental healthOperations managementNursingEngineeringGeography

Abstract

fetched live from OpenAlex

Excess sodium intake can lead to increased blood pressure. Restaurant foods contribute nearly a quarter of the sodium consumed in the American diet. The objective of the pilot project was to develop and implement in collaboration with independent restaurants a tool, the Restaurant Assessment Tool and Evaluation (RATE), to assess efforts to reduce sodium in independent restaurants and measure changes over time in food preparation categories, including menu, cooking techniques, and products. Twelve independent restaurants in Schenectady County, New York, voluntarily participated. From initial assessment to a 6-month follow-up assessment using the RATE, 11 restaurants showed improvement in the cooking category, 9 showed improvement in the menu category, and 7 showed improvement in the product category. Menu analysis conducted by the Schenectady County Health Department staff suggested that reported sodium-reduction strategies might have affected approximately 25% of the restaurant menu items. The findings from this project suggest that a facilitated assessment, such as the RATE, can provide a useful platform for independent restaurant owners and public health practitioners to discuss and encourage sodium reduction. The RATE also provides opportunities to build and strengthen relationships between public health care practitioners and independent restaurant owners, which may help sustain the positive changes made.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.829
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.096
GPT teacher head0.397
Teacher spread0.301 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations7
Published2013
Admission routes1
Has abstractyes

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