Benchmarking the healthiness, equity and environmental sustainability of university food environments in Australia, 2021/22
Bibliographic record
Abstract
BACKGROUND: Food environments on university campuses have an important influence on the diets of staff and students. This study aimed to assess the healthiness, equitability and environmental sustainability of Australian university food environments, and identify priority recommendations for policy and practice. METHODS: We applied the previously developed 'Uni-Food' tool in nine universities (17 campuses, 165 food retail outlets) in Australia between 2021 and 2022. Data on three components: (1) 'university systems and governance'; (2) 'campus facilities and environment'; and (3) 'food retail outlets' were collected from desk-based policy audits and in-person campus audits. Universities were given an overall score from 0-100, based on their performance across all components. RESULTS: University scores ranged from 27/100 to 66/100 (median = 46). Universities scored highest in the 'campus facilities and environment' component, reflecting that the broad campus environment (including areas such as catering, advertising on campus, and food-related environmental sustainability initiatives) has been an area of focus. Universities scored lowest in the 'university systems and governance' component, reflecting a relative lack of policy action, funding and governance in this area, with few initiatives to promote the availability and affordability of healthy and environmentally sustainable foods. CONCLUSION: Stronger action is needed to improve Australian university food environments, including in food retail outlets, vending, catering and at campus events. Universities can demonstrate leadership by implementing university-wide policies that limit the availability of unhealthy foods and beverages (e.g. sugary drinks) on campus, and setting targets for the proportion of healthy and environmentally sustainable foods procured and sold on campus. Other stakeholders, including governments, can play a role in incentivising universities to adopt recommended actions.
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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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".