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Record W6961000480 · doi:10.14288/1.0401362

Recreation Facility Food and Beverage Environments in Ontario, Canada: An Appeal for Policy

2021· article· en· W6961000480 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Collections · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural pest management studies
Canadian institutionsnot available
Fundersnot available
KeywordsAuditRecreationUnintended consequencesAppealFood supplyWater utility

Abstract

fetched live from OpenAlex

Canadian, municipally funded recreation/sport facilities typically have unhealthy food environments. Ontario, unlike some provinces, lacks a voluntary recreation facility nutrition policy. This study assessed the healthfulness of food environments and vending sales in 16 Ontario recreation/sport facilities and, secondarily, compared data from facilities within municipalities that banned versus permitted plastic bottled-water sales (water-ban, n = 8; water, n = 8) to test the nutritional effects of environmental policy. Concession and vending packaged food/beverage offerings and vending sales were audited twice, eighteen months apart. The products were categorized using nutrition guidelines as Sell Most (SM), Sell Sometimes (SS), and Do Not Sell (DNS). Both water and water-ban facilities offered predominantly (>87%) DNS packaged food items. However, proportions of DNS and SM concession and vending beverages differed (p < 0.01). DNS beverages averaged 74% and 88% of vending offerings in water and water-ban facilities, respectively, while SM beverages averaged 14% and 1%, respectively. Mirroring offerings, DNS beverages averaged 79% and 90% of vending sales in water versus water-ban facilities. Ontario recreation/sport facilities provided unhealthy food environments; most food/beverage offerings were energy-dense and nutrient-poor. Water bans were associated with increased facility-based exposure to DNS beverage options. A nutrition policy is recommended to make recreation facility food/beverage environments healthier and to mitigate unintended negative consequences of bottled-water bans.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.218
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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