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Record W4296916551 · doi:10.1159/000526465

Sustainability in Hospital Food Catering: How We Can Adapt to the New Reality

2022· article· en· W4296916551 on OpenAlexaboutno aff
Kalliopi Anna Poulia

Bibliographic record

VenueKompass Nutrition & Dietetics · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityBusinessQualitative researchMarketingHealth careDiffusion of innovationsPerceptionProcess managementNursingPsychologyMedicinePolitical scienceSociology

Abstract

fetched live from OpenAlex

Purpose: The healthcare sector is an important area for sustainable food initiatives, given its inherent mission to heal and its substantial impact on the food system. Foodservice managers can take part in these initiatives by using sustainable menu practices (SMPs). This study aimed to explore managerial perceptions of barriers and facilitators to adopting SMPs in Québec healthcare institutions. Methods: Seventeen foodservice managers were recruited through purposeful sampling to participate in a qualitative semi-structured interview. The Diffusion of Innovations theory was used to assess the main determinants of the diffusion of an innovation (SMPs) through a complex social system (healthcare organization). Results: Participants reported more barriers than facilitators. Lack of support at many levels was recognized as a major hindrance to SMP adoption, as were shortfalls in political directives. Increased collaboration between all food system actors and better communication in healthcare were perceived as needed for increased SMP adoption. Conclusions: This research contributes to an in-depth understanding of managerial experiences in SMP adoption in various regional and healthcare settings. Findings suggest the need for support and strategies that would remove important barriers for foodservice managers and contributed to the development of a guide to support foodservice managers in implementing SMPs.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.028
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.052
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0080.016
Scholarly communication0.0220.025
Open science0.0050.020
Research integrity0.0170.022
Insufficient payload (model declined to judge)0.0350.008

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 head0.018
GPT teacher head0.211
Teacher spread0.193 · 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

Labeled directly by 2 models reading the full record.

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

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
Published2022
Admission routes1
Has abstractyes

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