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Record W2102843017 · doi:10.3390/ijerph7052208

Publically Funded Recreation Facilities: Obesogenic Environments for Children and Families?

2010· article· en· W2102843017 on OpenAlexaffabout
Patti‐Jean Naylor, Laura Bridgewater, Megan Purcell, Aleck Ostry, Suzanne Vander Wekken

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2010
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsRecreationAuditContext (archaeology)Focus groupBusinessEnvironmental healthPublic healthMarketingPublic relationsMedicineGeographyNursingPolitical scienceAccounting

Abstract

fetched live from OpenAlex

Increasing healthy food options in public venues, including recreational facilities, is a health priority. The purpose of this study was to describe the public recreation food environment in British Columbia, Canada using a sequential explanatory mixed methods design. Facility audits assessed policy, programs, vending, concessions, fundraising, staff meetings and events. Focus groups addressed context and issues related to action. Eighty-eighty percent of facilities had no policy governing food sold or provided for children/youth programs. Sixty-eight percent of vending snacks were chocolate bars and chips while 57% of beverages were sugar sweetened. User group fundraisers held at the recreation facilities also sold 'unhealthy' foods. Forty-two percent of recreation facilities reported providing user-pay programs that educated the public about healthy eating. Contracts, economics, lack of resources and knowledge and motivation of staff and patrons were barriers to change. Recreation food environments were obesogenic but stakeholders were interested in change. Technical support, resources and education are needed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.550
Threshold uncertainty score0.905

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.047
GPT teacher head0.361
Teacher spread0.313 · 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 source (direct Gemma or distilled Codex), 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

Citations54
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Environmental Research and Public HealthSame topicObesity, Physical Activity, DietFrench-language works237,207