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Record W2618352800 · doi:10.1097/phh.0000000000000555

Ciclovia in a Rural Latino Community: Results and Lessons Learned

2017· article· en· W2618352800 on OpenAlexaff
Cynthia K. Perry, Linda K. Ko, L. Ureña Hernández, Rosa Amalia Gómez Ortíz, Sandra Linde

Bibliographic record

VenueJournal of Public Health Management and Practice · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsLinde (Canada)
FundersNational Institute on Minority Health and Health Disparities
KeywordsData scienceEnvironmental healthPsychologyComputer scienceMedicine

Abstract

fetched live from OpenAlex

CONTEXT: Ciclovias involve the temporary closure of roads to motorized vehicles, allowing for use by bicyclists, walkers, and runners and for other physical activity. Ciclovias have been held in urban and suburban communities in the United States and Latin America. OBJECTIVE: We evaluated the first ciclovia held in a rural, predominantly Latino community in Washington State. SETTING: Three blocks within a downtown area in a rural community were closed for 5 hours on a Saturday in July 2015. OUTCOME MEASURES: The evaluation included observation counts and participant intercept surveys. RESULTS: On average, 200 participants were present each hour. Fourteen percent of youth (younger than 18 years) were observed riding bikes. No adults were observed riding bikes. A total of 38 surveys were completed. Respondents reported spending on average 2 hours at the ciclovia. Seventy-nine percent reported that they would have been indoors at home involved in sedentary activities (such as watching TV, working on computer) if they had not been at the ciclovia. CONCLUSION: Regularly held ciclovias, which are free and open to anyone, could play an important role in creating safe, accessible, and affordable places for physical activity in rural areas. Broad community input is important for the success of a ciclovia.

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)0.023
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.003
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 head0.298
GPT teacher head0.483
Teacher spread0.186 · 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.

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

Citations13
Published2017
Admission routes1
Has abstractyes

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