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Record W2162566709 · doi:10.1136/ip.2010.029215.450

The effect of a celebrity death on children's injury-related emergency department visits

2010· article· en· W2162566709 on OpenAlexaffabout
Glenn Keays, I B Pless

Bibliographic record

VenueInjury Prevention · 2010
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsMcGill University Health CentreMontreal Children's Hospital
Fundersnot available
KeywordsEmergency departmentMedicineInjury preventionPoison controlOccupational safety and healthHead injuryMedical emergencySuicide preventionEmergency medicineDemographyPsychiatry

Abstract

fetched live from OpenAlex

Background Following a skiing-related head injury death of the actor Natasha Richardson, we noted a marked increase in injury-related visits to the Emergency Department (ED) of the Montreal Children's Hospital (MCH). We assumed these visits were driven by media coverage, which would be greater in Quebec than elsewhere in Canada. Methods Using data from the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP), we compared visits to the MCH-ED for 10 weeks beginning in March. We also compared visits for the event week with the averages for the same week in the preceding 16 years. We noted the percent of MCH-ED visits that were for head injuries. Finally, we examined the same 10-week patterns for children's hospitals in Toronto, Calgary and Vancouver. Results We found a 60% increase at the MCH for the event week compared with the first week of March and 66% increase compared to the 16-year average. We noted that for the event week 43.7% of injury visits were for head injuries compared with 28.8% (16-years average). There was, however, no change in ratio of severe head injuries. The increase in Toronto, Calgary and Vancouver was 24%, 23% and 22% respectively over the first week of March. Interpretation These data suggest that the media coverage of this celebrity death which involved a failure to seek medical attention, generated anxiety among parents, prompting them to bring children to the ED who might not otherwise have sought medical care.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.006
GPT teacher head0.304
Teacher spread0.298 · 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
Published2010
Admission routes2
Has abstractyes

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