Barriers and Facilitators to Implementation of Healthy Food and Drink Policies in Public Sector Workplaces: A Systematic Literature Review
Bibliographic record
Abstract
Many countries and institutions have adopted policies to promote healthier food and drink availability in various settings, including public sector workplaces. However, studies reporting barriers and facilitators experienced by food vendors and caterers in providing healthy and nutritious foods and drinks have not been collated and synthesised, representing a significant gap in workplace health promotion knowledge. Our objective was to systematically synthesise evidence on barriers and facilitators relative to the implementation of and compliance with healthy food and drink policies aimed at the general adult population in public sector workplaces internationally. Nine scientific databases, nine grey literature sources, and government websites in key English-speaking countries were searched between April and June 2021. All identified records (n = 8559) were assessed for eligibility. Studies reporting barriers and facilitators were included irrespective of the study design and methods used, but they were excluded if they were published before the year 2000 or in a non-English language. Methodological strengths and limitations of the included studies were assessed with the CASP Qualitative Studies Checklist. Drawing on a thematic synthesis approach, primary findings were generated through research question-led coding and theme development. Forty-one studies were eligible for inclusion, and they were mainly from Australia, the United States, and Canada. The most common workplace settings were healthcare facilities, sports and recreation centres, and government agencies. Generally, poorly reported data collection and analysis methods were observed. Preliminary findings suggest that although vendors encounter challenges, there are also factors that support healthy food and drink policy implementation in public sector workplaces. Generated codes indicate that barriers and facilitators fall into five broad categories of financial ramifications, availability of healthier products, existence of supporting tools and resources, institutional leadership support, and communication between stakeholders. Understanding barriers and facilitators to successful policy implementation will significantly benefit stakeholders interested in or engaging in healthy food and drink policy development and 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 | 0.035 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".