Exploring the relationship between boil water advisories and beverage attitudes and intake in adults in Newfoundland and Labrador
Bibliographic record
Abstract
Newfoundland and Labrador (NL) became the first Canadian province to implement a tax on sugar sweetened beverages (SSBs) in 2022 as an effort to reduce consumption and lower the rate obesity and diabetes. NL also launched a “Rethink Your Drink” campaign to promote water as the drink of choice. However, there may be barriers to choosing water such as the presence of boil water advisories and negative attitudes towards tap water. The objective of this study is to explore the factors influencing total water consumption in NL and assess the impact of attitudes towards tap water and boil water advisories as a predictor of total water and SSB intake. We conducted a cross-sectional online study with adults in NL before and after the implementation of the SSB tax. Attitudes towards tap water and perceptions about being under a boil water advisory impacted beverage consumption. Having negative attitudes towards tap water predicted lower total water consumption (−221 mL/week, p = 0.002) as well as reported being under a boil water advisory (−213 mL/week, p = 0.042), after accounting for sociodemographic variables. Having negative attitudes towards tap water was not a significant predictor of SSB intake after controlling for sociodemographic factors ( p = 0.090). Positive health impacts of the NL SSB tax may not be fully realized if SSBs are not substituted for healthier beverage choices, such as water. Policy makers should be aware of the relationship between negative attitudes towards tap water, boil water advisories, and beverage consumption when implementing initiatives to improve public health.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".