Regional survey supports national initiative for ‘water‐only’ schools in New Zealand
Bibliographic record
Abstract
OBJECTIVE: To support a national initiative to remove sugary drinks from schools and limit drinks to water or unflavoured milk ('water-only'). METHODS: We emailed all 201 schools with primary school aged children in the Greater Wellington region with a survey on (1) current status of, (2) support needs for, and (3) barriers to or lessons learned from, a 'water-only' school policy. RESULTS: Only 78 (39%) of schools responded. Most supported 'water-only': 22 (28%) had implemented a policy; 10 (13%) in process of doing so; 22 (28%) were considering it; and 12 (15%) were 'water-only', but did not have a policy. Only 12 (15%) were not considering a 'water-only' policy. The main barrier reported was lack of community and/or family support. Many schools did not see any barriers beyond the time needed for consultation. Monitoring and communication were identified as key to success. A quarter of schools requested public health nurse support for a 'water-only' policy. CONCLUSIONS: The survey elicited a range of views on 'water-only' policies for schools, but suggests that 'water-only' may be an emerging norm for schools. Implications for public health: Our survey shows how local assessment can support a national initiative by providing a baseline, identifying schools that want support, and sharing lessons. Making schools 'water-only' could be a first step in changing our children's environment to prevent obesity.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".