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PW 2289 Injury risk assessment in schools in karachi, pakistan

2018· article· en· W2892936192 on OpenAlexaboutno aff
Uzma Khan, Rubaba Naeem, Nukhba Zia

Bibliographic record

VenueAbstracts · 2018
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Occupational safety and healthQuarter (Canadian coin)Injury preventionRisk assessmentEnvironmental healthPoison controlMedicineEngineeringForensic engineeringMedical educationGeographyComputer securityComputer science

Abstract

fetched live from OpenAlex

Studies from low- income countries have shown that 7% of all childhood injuries occur in schools; however little is known about the injury hazards present in the school environment. The objective of this study was to develop and pilot injury risk assessment tool in the schools in Karachi. The study was conducted from March – June 2013. These risk observations in school environment were done by trained data collectors. The injury risk assessment was done in 107 schools. About a quarter (26%) of classrooms had broken furniture. In 50.9% of schools the playground surface was of concrete. About 28.3% schools had low height of corridor walls, while 60.4% schools had open wires in electrical switches. In 14.2% of schools there were cleaning chemicals in toilets within the reach of children. There were stray dogs within schools (22.4%). This was the first time that injury school environment risks tool was developed and piloted in the context of Pakistan. There was a significant burden of hazards present in the school environment representing an important opportunity for injury prevention.

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)0.002
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 score0.025
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.393
Teacher spread0.374 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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