Copyright © 2015 Donnish Journals
Bibliographic record
Abstract
Background: Injuries remain a major killer of children throughout the world. On average, for children in the age group 5-14 years, injury accounts for more than a quarter (27 %) of all deaths worldwide. In addition to these deaths, many other children sustain injuries that require hospitalization, outpatient treatment and sometimes result in disability. In Kenya, injuries are the third leading cause of mortality after malaria and HIV/AIDS and are the fifth leading cause of morbidity among patients attending health care facilities. Approximately 16.8 % of reported injury cases in Kisumu occur among children less than 15 years of age and accounting for 28.7 % of the total injury admissions. Although children less than 15 years of age fall within the primary school age, not much is known about the nature and factors that contribute to the occurrence of injuries among primary school children in Kisumu and programs focusing on their prevention are lacking. Methods: This was a cross-sectional study that aimed to describe the characteristics of injuries among primary school children in Kisumu Municipality. A random sample of 492 pupils aged 11 to 18 years; from 18 schools was interviewed using a structured questionnaire to collect information on the incidence of injuries during the three month period prior to the interview. Results: The most common cause of these injuries was due to falls on the same level (33.9 %). Majority of these pupils were injured while playing (48.8 %), at home (40.9 %) with cuts or open wounds being the most common injury that was
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.779 | 0.705 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".