MétaCan
Menu
Back to cohort
Record W1632511601 · doi:10.1093/pch/18.8.425

The importance of child and youth death review

2013· article· en· W1632511601 on OpenAlexaboutno aff
Amy Ornstein, Matthew Bowes, Michelle Shouldice, Natalie L Yancha

Bibliographic record

VenuePaediatrics & Child Health · 2013
Typearticle
Languageen
FieldMedicine
TopicAutopsy Techniques and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsHomicideLegislationContext (archaeology)Intervention (counseling)MandateMedicineCriminologySuicide preventionOccupational safety and healthPoison controlPolitical scienceFamily medicinePsychologyPsychiatryMedical emergencyLawGeography

Abstract

fetched live from OpenAlex

The mandate of a formal child death review (CDR) system is to advance understanding of how and why children die, to improve child health and safety, and to prevent deaths and injuries in the future. Areas in which CDR has provided valuable information and/or intervention include sudden death in infancy, unintentional injuries (the leading cause of death in Canadian children and youth one to 19 years of age), suicide in youth, and deaths due to homicide or child maltreatment. When collected systematically using common definitions, information regarding deaths in children and youth can help with understanding the scope of problems. Information about the context of a death can inform potential prevention or intervention activities. CDR can improve medical and mental health best practices, child welfare policies and procedures, and legislation and education relevant to public health and safety. In the United States, the United Kingdom, Australia and New Zealand, CDR processes are mandated by legislation. In Canada, death review teams have diverse structures and functions, and the CDR system is less well developed. The present statement addresses the need for formal, organized child and youth death review in Canada to help strengthen and systemize injury and death prevention efforts.

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)0.000
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: Review · Consensus signal: Review
Teacher disagreement score0.311
Threshold uncertainty score0.324

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.018
GPT teacher head0.302
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations10
Published2013
Admission routes1
Has abstractyes

Explore more

Same venuePaediatrics & Child HealthSame topicAutopsy Techniques and OutcomesFrench-language works237,207