Epidemiology and perceptions of non-fatal burns among select youth (15–24 years old) from Peshawar Pakistan; a sequential explanatory mixed methods study
Bibliographic record
Abstract
Burns are among the top five cause of unintentional injuries among youth. We aimed to determine the incidence and risk factors of burns in select youth from Pakistan and also explored their perceptions about burns. A sequential explanatory mixed methods study was conducted which comprised of two phases. Phase 1 consisted of a cross-sectional quantitative (quan) survey in which 550 youth (15 to 24 years-old) filled out the WHO community survey tool for injuries and violence and provided data on burns in previous 12 months (2021–22). Youth from four vocational training institutes in Peshawar participated in the study. Phase 2 qualitative (QUAL), comprised of in-depth interviews (IDIs) with participants selected from phase 1 based on the occurrence of burns. STATA™ version 15.1 was used for Phase 1 data analysis and Zero-Inflated Negative Binomial model was used for determining the incidence rate ratio (IRR) of burns. Thematic analysis was performed for interviews from Phase 2. Out of 547 participants 356 (65%) were males while 191 (35%) were females. Burns were reported by 85 participants (15%) while 137 counts of burns were reported. The IRR of burns among young females was 2.89 (1.78–4.69) compared to males in 2021–22. Age, sex, father’s education, and family income were significant risk factors for burns. Six IDIs were conducted with burn victims, two were males while six were females. Four cooking-related burns, and two occupations burns were reported. Two themes that emerged from IDIs were personal carelessness and lack of knowledge and safety. Two sub-themes that were significant for recovery from burns were family support and access to healthcare. Young females are at a higher risk of burn injuries in Pakistan. Various social, demographic, and behavioral risk factors are associated with burns. Pre-burn and post-burn interventions are needed for prevention from burns in youth.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".