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Record W2904793868 · doi:10.1186/s40621-018-0177-4

Building the injury field in North America: the perspective of some of the pioneers

2018· editorial· en· W2904793868 on OpenAlexfundno aff
David Hemenway

Bibliographic record

VenueInjury Epidemiology · 2018
Typeeditorial
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsnot available
FundersNational Institute on Alcohol Abuse and AlcoholismTufts University School of MedicineConnecticut Children's Medical CenterUniversity of PittsburghJohns Hopkins UniversityHarvard T.H. Chan School of Public HealthNorthwestern UniversityChildren's Hospital of PhiladelphiaCenters for Disease Control and PreventionSeattle Children's Research InstituteMcGill UniversityUniversity of MinnesotaYale UniversityU.S. Department of Health and Human Services
KeywordsPublic healthMedicineInjury preventionOccupational safety and healthSuicide preventionPoison controlBiostatisticsAgency (philosophy)Human factors and ergonomicsLimitingMedical emergencyEnvironmental healthFamily medicineNursingEngineeringSociologyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: After the publication in 1985 of Injury in America and the establishment of an injury center at the Centers for Disease Control, there was a concerted attempt to create an "injury field." MAIN BODY: Thirty-six (36) pioneers in the injury prevention field responded to questions about the major accomplishments and failures of their profession since the publication of the seminal Institute of Medicine report Injury in America in 1985. Much has been accomplished. Indeed, it is difficult to believe that before the 1990s there was no federal agency focused on preventing fall injuries, drownings, sport concussions or bullying in schools. There was no readily available surveillance data on fatal injuries, no national associations of injury researchers or practitioners, no American Public Health Association (APHA) injury and emergency health services (ICEHS) section and few injury journals. Hardly anyone wore seatbelts and virtually no cigarettes were fire-safe. Sadly, there has been little success at limiting firearm and overdose deaths as injury prevention remains a step-child in the health field with funding not nearly commensurate to the size of the problem. Training in effective advocacy has been proposed both to help attract funding and reduce injuries. CONCLUSION: Injury prevention pioneers have much to teach current public health students, researchers and practitioners about the history and future of the field.

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)0.007
metaresearch head score (Gemma)0.063
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.187
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.063
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.003
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 head0.020
GPT teacher head0.371
Teacher spread0.351 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations8
Published2018
Admission routes1
Has abstractyes

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