Tools and resources used to support implementation of workplace healthy food and drink policies: A scoping review of grey literature
Bibliographic record
Abstract
AIMS: This study aimed to identify and evaluate tools and resources used to support the implementation of workplace healthy food and drink policies, primarily in Australia and New Zealand. METHODS: A scoping grey literature review included searches of government agencies and non-governmental organisations' websites in six English-speaking countries, public health nutrition intervention databases and Google search engine queries. Paper-based and digital tools were included if they were written in English, referred to within a policy or on a policy's website, and primarily targeting supply-side stakeholders. Tools were evaluated on two domains: 'Features' (summarised descriptively) and 'Usability and Quality' (with inter-rater reliability scores calculated using an intraclass correlation coefficient). RESULTS: Twenty paper-based tools were identified relating to Australian (n = 14) and New Zealand (n = 6) policies, and a further six digital tools were identified from Australia (n = 3) and Canada (n = 3). Target audiences included workplace managers, food providers and suppliers. The paper-based tools focused on general implementation guidance. In contrast, digital tools tended to support specific elements of policy implementation. 'Usability and Quality' scores ranged from 2.9 to 4.5 (out of 5.0) for paper-based tools, and 3.9 to 4.2 for digital tools, with a moderate agreement between reviewer scores (intraclass correlation coefficient 0.523, p = 0.010). CONCLUSIONS: A range of tools have been developed to support the implementation of workplace healthy food and drink policies. Understanding the strengths and limitations of current tools will assist in developing improved aids to support policy implementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.053 | 0.159 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.043 | 0.031 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".