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Record W2089817794 · doi:10.1353/hpu.0.0226

Unintentional Childhood Injuries in Sub-Saharan Africa: An Overview of Risk and Protective Factors

2009· review· en· W2089817794 on OpenAlex
Mónica Ruiz‐Casares

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Health Care for the Poor and Underserved · 2009
Typereview
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsMcGill UniversityCentre de Santé et de Services Sociaux de la Montagne
Fundersnot available
KeywordsSocioeconomic statusEnvironmental healthPsychological interventionMedicineInjury preventionOccupational safety and healthContext (archaeology)Poison controlEpidemiologyHuman factors and ergonomicsSuicide preventionDeveloping countryPopulationGeographyEconomic growthNursing

Abstract

fetched live from OpenAlex

The rate of unintentional injuries for children in sub-Saharan Africa has reached 53.1 per 100,000, the highest for regions across all income levels. This paper reviews the relevant literature on the epidemiology of unintentional childhood injuries in the region, with an emphasis on the risk factors associated with it. Several demographic, socioeconomic, and environmental factors contributing to injuries in children have been documented for the main causes of injury. Despite the high burden, child injury prevention and control programs and policies are limited or non-existent in many countries in the region. Accurate data regarding these injuries across and within countries is incomplete. Population-based estimates and investigations into context-specific risk factors, safety attitudes, and behaviours are needed to inform the development of effective interventions.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.133
GPT teacher head0.410
Teacher spread0.277 · 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