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Record W2790321311 · doi:10.5864/d2018-008

An assessment of child care facility playground surfacing safety in Calgary, Alberta

2018· article· en· W2790321311 on OpenAlexaffvenueabout
Dana East, Karla Gustafson, Jason Cabaj, Lynne Navratil

Bibliographic record

VenueEnvironmental Health Review · 2018
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsUniversity of CalgaryAlberta Health Services
Fundersnot available
KeywordsLegislationOccupational safety and healthTest (biology)Environmental healthForensic engineeringEngineeringEnvironmental planningEnvironmental scienceMedicinePolitical science

Abstract

fetched live from OpenAlex

Absorption capacity of playground surfaces is a well-recognized factor in injury prevention as most playground injuries result from falls on playground surfaces. In the past, Environmental Health Officers had no way of evaluating impact absorption of playground surfaces so injury prevention capacities of surfacing were unknown. To assess injury prevention characteristics of playground surfaces in the Calgary Zone, Environmental Public Health began testing surfaces in 2012 with a Triax Surface Impact Tester. A total of 102 playground surfaces were tested to the end of 2016. Forty-five (44%) playgrounds failed the impact absorption test, indicating falls from the equipment onto these surfaces could result in a life-threatening head injury. These findings suggest a large percentage of playground surfaces are not providing adequate fall safety. Playground owners/operators require additional knowledge and resources to inform decisions about playground surfacing, and changes to public health legislation should be considered to require formal assessment of playgrounds and ensure playground surfacing is addressed in a consistent manner.

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)0.002
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.171

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.293
Teacher spread0.284 · 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

Citations1
Published2018
Admission routes3
Has abstractyes

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