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Record W2091543104 · doi:10.5430/jha.v4n3p20

Barriers to sustaining customer participation in hospital-based farmers’ markets: insights from employees

2015· article· en· W2091543104 on OpenAlexvenueno aff
Daniel R. George, Jennifer L. Kraschnewski, Liza S. Rovniak, Lindsay Vaughn, Judy Dillon

Bibliographic record

VenueJournal of Hospital Administration · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Institutes of Health
KeywordsAttendanceBusinessWork (physics)MarketingEconomic growthEconomics

Abstract

fetched live from OpenAlex

92 farmers’ markets are located on hospital campuses in the United States but no known studies have evaluated factors influencing employee use of on-site markets. We examine modifiable barriers that reduced employee participation in a hospital-based market at Pennsylvania State Hershey Medical Center. 360 employees of Pennsylvania State Hershey Medical Center who used a weekly on-site seasonal market less than twice annually were sent an online survey, and frequency of response data were analyzed. Most frequently referenced barriers to participation were: location/access to the market, personal work schedules, cost of market products, and hours of operation, while top perceived benefits were support of local agriculture, health benefits, atmosphere/environment, and affordability. Hospital markets using value-based marketing campaigns to promote local economic and individual health benefits of participation, maximize convenience and access, and incentivize attendance are likely to sustain employee participation. These modifiable features may be relevant to worksite markets in diverse regions.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
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 head0.013
GPT teacher head0.250
Teacher spread0.236 · 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 designQualitative
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

Citations0
Published2015
Admission routes1
Has abstractyes

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