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Record W2336838223 · doi:10.5993/ajhb.40.3.9

Stakeholder Perspectives on Implementing Menu Labeling in a Cafeteria Setting

2016· article· en· W2336838223 on OpenAlexaff
Lana Vanderlee, Michelle M. Vine, Nancy Fenton, David Hammond

Bibliographic record

VenueAmerican Journal of Health Behavior · 2016
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCafeteriaChampionStakeholderQualitative researchTurnoverAdaptation (eye)Process (computing)BusinessProcess managementPublic relationsKnowledge managementComputer sciencePsychologyMedicineManagementSociology

Abstract

fetched live from OpenAlex

OBJECTIVES: Mandatory and voluntary menu-labeling policies are increasingly common to support informed food choices among consumers. This study sought to examine stakeholder perspectives of developing, implementing, and maintaining a voluntary menu-labeling program in a hospital cafeteria setting. METHODS: Semi-structured qualitative interviews were conducted with 9 key cafeteria stakeholders. Data were coded by 2 independent researchers. Themes were generated deductively around 4 key themes: (1) motivation for the program; (2) program and menu development; (3) program implementation process; and, (4) "lessons learned," and inductively as they emerged from interview transcripts. These themes were mapped onto Damschroder's Consolidated Framework for Implementation Research. RESULTS: Motivations for the program were both internal and external to enable consumers to make educated food choices. Barriers to implementation included financial resources, digital menu board maintenance, and availability of healthy options from providers. Supports included availability of nutritional analysis software and nutritional information, and controlled food preparation. Ownership, program adaptation, a supportive collegial environment, a program champion and a culture valuing healthy eating were conducive to successful implementation. CONCLUSIONS: Both internal and external factors can support the voluntary implementation of menu-labeling programs.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.732
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.055
GPT teacher head0.374
Teacher spread0.319 · 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 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

Citations5
Published2016
Admission routes1
Has abstractyes

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