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“What do Kids Know”: A Survey of 420 Grade 5 Students in Cambodia on their Knowledge of Burn Prevention and First-Aid Treatment

2006· article· en· W2001235863 on OpenAlexaffabout
Marvin Hsiao, Brian Tsai, Pisey Uk, Hwayeon Jo, Manuel Gómez, James Gollogly, M. Beveridge

Bibliographic record

VenueJournal of Burn Care & Research · 2006
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineFirst aidFamily medicineBurn injuryDeveloping countryInjury preventionPoison controlMedical emergencySurgery

Abstract

fetched live from OpenAlex

Cambodia is a developing country of 13 million people where there are an estimated 20,000 burn injuries and 2000 burn deaths annually. Two thirds of the burns occur to children under the age of ten. Since 2000, members of a Canadian regional burn centre have been providing training for Khmer surgeons, nurses, and therapists at an NGO hospital that provides free treatment for disabled people in Phnom Penh, Cambodia. The purpose of this study was to determine the knowledge of burn safety and first aid in Grade 5 school children, as a baseline information to design a burn prevention campaign. A 34-question survey was developed to ascertain the knowledge of Cambodian school children regarding burn prevention and first-aid treatment for burn injuries. Additional questions on TV watching habits were included to determine the feasibility of a targeted TV burn education campaign. The survey was translated into Khmer and back-translated into English and pre-tested on a trial class. Informed consent was obtained and the surveys were administered in class with help from trained translators and the teacher.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.051
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.081
GPT teacher head0.430
Teacher spread0.350 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2006
Admission routes2
Has abstractyes

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