Epidemiology of Injury Among School-Going Adolescents in Ghana: Evidence From the Global School-Based Students Health Survey
Bibliographic record
Abstract
Abstract Background: Unintentional injuries are a serious global public health problem and the second leading cause of death among adolescents age between 10-19 years. It is also the least researched in most low-income countries and Ghana in particular. This study aims to provide estimates of the prevalence, causes, and associated risk factors of unintentional injuries among school-going adolescents in Ghana.Methods: This is a cross-sectional study using data from the Ghana Global School-based Student Health Survey conducted in 2012 (N=3632). A two-stage cluster probability sampling technique was employed to select a representative sample of Junior and Senior High School students in ten administrative regions of the country. Cross tabulations and chi-square tests were used to provide estimates of prevalence, frequency, and causes of injuries. In the multivariable stage, a multiple logistic regression based on SAS 9.4 survey logistic modeling procedure was conducted to examine the association between unintentional injuries with explanatory variables. The final adjusted model was assessed using a significant level of (p<0.05).Results: In this study, 40.96% (1488) of school-aged adolescents reported having experienced an unintentional injury. Of these injuries, 64.5% occurred at home, while the least occurred via traffic (15.0%). The frequency of injury did not differ between boys and girls (P=0.368), but significantly higher in adolescents aged ≤17 than in those ≥ 18 years (P<0.001). Also, injuries were significantly higher in junior high school than in senior high school adolescents. Age, physical activity, sleeping problems, and loneliness were found to be the significant risk factors associated with unintentional injury among adolescents.Conclusion: Prevalence of unintentional injuries were widespread among adolescents in Ghana. Prompt prevention of these injuries among adolescents should be a public health priority. Preventive strategies in schools and homes should be enhanced. Injury preventive measures should emphasize on adolescents. Treatment options should be made available to adolescents struggling with a sleeping problem, loneliness, and those with accidents in physical activity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".