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Record W4408086538 · doi:10.51250/jheal.v5i1.85

Users’ safety perceptions from crime in relation to park type and user gender in Mexico.

2025· article· en· W4408086538 on OpenAlexaff
Julissa Ortiz Brunel, Edtna Jáuregui-Ulloa, A. Comfort, Pedro Júarez-Rodríguez, Rebecca E. Lee, Juan R Lopez y Taylor, José Marcos Pérez-Maravilla, Iván Zarate, Lucie Lévesque

Bibliographic record

VenueJournal of Healthy Eating and Active Living · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsQueen's University
Fundersnot available
KeywordsRelation (database)PerceptionPsychologyGeographySocial psychologyComputer scienceData mining

Abstract

fetched live from OpenAlex

While parks hold potential as inclusive spaces for promoting physical activity, perceptions of safety from crime may affect their use, especially in low-to middle-income countries. Safety perceptions may be shaped by gender and park type; however, these relationships have not been explored in Mexico. The aim of this study was to explore associations between safety perceptions and park type by gender. This was a cross-sectional and descriptive study. We assessed perceptions of safety from crime in Mexican adult park users in Jalisco state. Six parks were classified into three categories: 1) Metropolitan parks with controlled gate access (gated), 2) Metropolitan parks without controlled gate access (open), and 3) Linear parks (linear). We ran binary logistic regression models to investigate the association between safety perception and park type, and safety perception and gender. We found that men were more likely to feel safe than women, regardless of park type, and users of linear parks were more likely to feel safe than users of gated parks, regardless of gender. Safety perception is related to park type and park user gender. Future studies should explore which specific park attributes are influencing park user safety perception and how to address gender disparities.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.015
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.049
GPT teacher head0.397
Teacher spread0.348 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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