Knowledge, Attitude, and Practice Regarding Cell Phone Radiation Health Hazards Among Jami University Students
Bibliographic record
Abstract
Objectives: This study aimed to evaluate the KAP (Knowledge, Attitude, and Practice) regarding cell phone radiation health hazards among students at Jami University, Herat, Afghanistan, to identify gaps and promote safer usage practices. Methods: A descriptive cross-sectional study was conducted among 350 students selected through stratified random sampling during September and October 2024. A structured KAP questionnaire assessed participants' knowledge, attitudes, and practices regarding cell phone radiation risks. Data were analyzed using SPSS v.27. Results: The mean knowledge score was 74.03, with moderate awareness of general risks but limited understanding of technical concepts such as Specific Absorption Rate values (36.6% awareness). Attitudes reflected significant concern, with most students associating cell phone radiation with sleep disorders (64.9%), hearing impairments (65.4%), and brain tumors (56.0%). However, practices were inconsistent; 62.0% minimized call durations, yet 70.3% carried phones in pockets, and 54.3% slept with phones nearby. Poor compliance with precautionary measures, such as using hands-free devices (50% rarely used), highlighted behavioral gaps. Conclusion: While Jami University students exhibited moderate knowledge and proactive attitudes toward cell phone radiation risks, their practices revealed significant gaps. Enhanced educational initiatives targeting technical knowledge and practical safety behaviors are essential. Integrating radiation safety into curricula and conducting awareness campaigns can bridge these gaps, fostering informed and consistent adherence to safer practices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".