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Record W4388115234 · doi:10.1016/j.jneb.2023.10.008

Acceptability and Feasibility of a Hospital-Based Herb and Vegetable Garden for Health Care Workers

2023· article· en· W4388115234 on OpenAlexvenueno aff
C. Carroll, Sally McCray, Jennifer Utter

Bibliographic record

VenueJournal of Nutrition Education and Behavior · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsPsychosocialBaseline (sea)WorkforceNursingEmployee engagementKey (lock)Service (business)MedicinePsychologyEnvironmental healthBusinessMarketingPublic relationsPolitical science

Abstract

fetched live from OpenAlex

OBJECTIVE: Evaluate the acceptability and feasibility and explore the potential health impacts of a hospital-based herb and vegetable garden. METHODS: Mixed-method program evaluation assessed dietetic and food service staff health, well-being, and garden engagement. Surveys were administered at baseline and follow-up (6 months). Key informant interviews (n = 6) were conducted at 6 months to evaluate program feasibility. RESULTS: There was good acceptance and engagement with the garden, with 18 participants volunteering to maintain the garden. Key informant interviews identified workforce, leadership, and garden design engagement factors. Participants also noted several psychosocial benefits. CONCLUSION AND IMPLICATIONS: A hospital-based garden for staff is feasible if programmatic improvements are addressed. More robust evaluations considering challenges with measuring key outcomes with survey methodology and extended periods are warranted.

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)0.011
metaresearch head score (Gemma)0.015
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
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 head0.021
GPT teacher head0.308
Teacher spread0.287 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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