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Can child injury prevention include healthy risk promotion?

2014· review· en· W2142675288 on OpenAlexafffundabout
Mariana Brussoni, Sara Brunelle, Ian Pike, Ellen Beate Hansen Sandseter, Susan Herrington, Heather Turner, Scott Belair, Louise Logan, Pamela Fuselli, David J. Ball

Bibliographic record

VenueInjury Prevention · 2014
Typereview
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsParachuteUniversity of British Columbia
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BCChild and Family Research Institute
KeywordsRecreationOccupational safety and healthInjury preventionSuicide preventionPoison controlPromotion (chess)Multidisciplinary approachHuman factors and ergonomicsMedicinePublic relationsPsychologyEngineeringMedical educationPolitical scienceEnvironmental healthPathology

Abstract

fetched live from OpenAlex

To reflect on the role of risk-taking and risky play in child development and consider recommendations for the injury prevention field, a symposium was held prior to the November 2013 Canadian Injury Prevention and Safety Promotion Conference. Delegates heard from Canadian and international researchers, practitioners and play safety experts on child development, play space design and playground safety, provision of recreation, and legal and societal perceptions of risk and hazard. The presenters provided multidisciplinary evidence and perspectives indicating the potential negative effect on children's development of approaches to injury prevention that prioritise safety and limit children's opportunities for risky play. Delegates considered the state of the field of injury prevention and whether alternative approaches were warranted. Each presenter prepared a discussion paper to provide the opportunity for dialogue beyond attendees at the symposium. The resulting discussion papers provide a unique opportunity to consider and learn from multiple perspectives in order to develop a path forward.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0010.001

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 head0.045
GPT teacher head0.422
Teacher spread0.377 · 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; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreReview

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

Citations63
Published2014
Admission routes3
Has abstractyes

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