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Record W6981849608

A formative evaluation of the Eat Smart!: workplace Cafeteria Program at St. Peter's Hospital

2003· dissertation· en· W6981849608 on OpenAlexaboutno aff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2003
Typedissertation
Languageen
FieldSocial Sciences
TopicAsian Studies and History
Canadian institutionsnot available
Fundersnot available
KeywordsCafeteriaFormative assessmentProgram evaluationBehavior changeBehaviour changeProcess (computing)
DOInot available

Abstract

fetched live from OpenAlex

The aim of this study was to conduct a formative evaluation of the Eat Smart! Workplace Cafeteria Program in a hospital cafeteria in Hamilton, Ontario. Staff were primarily nurses and women between the ages of 40 and 50. The respondents' level of awareness of the program, attitudes towards the program, intentions to change behaviours and short-term behaviour change were assessed using a self-administered, mailed survey. The Tailored Design Method (Dillman, 2000) was used, resulting in a 51% response rate. Most respondents purchased meals, snacks and beverages regularly in the cafeteria, suggesting that exposure to the program was fairly high. Respondents identified many benefits of the program; they also suggested areas of the program that could be further developed and improved. This evaluation is a precursor to other evaluations of this program (i.e., other process and outcome evaluations), and will inform the development and implementation of other cafeteria and similar 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 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)0.030
metaresearch head score (Gemma)0.037
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 score0.030
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.037
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0030.002
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 head0.017
GPT teacher head0.264
Teacher spread0.246 · 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

Citations0
Published2003
Admission routes1
Has abstractyes

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