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Record W2152861294 · doi:10.1080/15389588.2011.561454

Comparison of Highway Crash Reporting in Pakistan With the World Health Organization Injury Surveillance Guidelines

2011· article· en· W2152861294 on OpenAlexaff
Ajmal Khan Khoso, Diana Ekman, Junaid A. Bhatti

Bibliographic record

VenueTraffic Injury Prevention · 2011
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsCrashOccupational safety and healthInjury preventionPoison controlSuicide preventionHuman factors and ergonomicsTransport engineeringBusinessForensic engineeringEngineeringMedical emergencyEnvironmental healthMedicineComputer science

Abstract

fetched live from OpenAlex

OBJECTIVES: To compare the crash reporting system of National Highways & Motorways Police (NH&MP), Pakistan, with the World Health Organization (WHO) injury surveillance guidelines. METHODS: Based on information collected from field observations, key informant interviews, and review of official documents, this note firstly describes the reporting system according to the components of a surveillance system. Then the reporting is compared with WHO criteria for designing and building an injury surveillance system and attributes of such a system. RESULTS: After a crash, a patrol officer communicates the information to the higher police authorities by wireless, fax, and on paper in the first 24 hours. Microcomputer Accident Analysis Package (MAAP) Performa filed by the officers are collected at a central location in the following 4 days, and reports are published biannually. Notable deficiencies in the reporting were nonidentification of stakeholders for data utilization and limited prospects of data recording process modification and its monitoring. Moreover, crash and injury definitions do not conform to international standards practiced elsewhere. CONCLUSION: NH&MP crash reporting needs to be simplified and standardized, and steps should be taken to improve its utilization for prevention purposes.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.090
GPT teacher head0.423
Teacher spread0.333 · 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

Citations10
Published2011
Admission routes1
Has abstractyes

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