AI for Qualitative Analysis of E-waste Practices

According to Scientific Reports, the 26 July 2026 mixed-methods study found that 57.9% of households in Kombolcha City had poor e-waste management practices. The study reports 60% of participants had poor knowledge and 58.2% had unfavorable attitudes in 2025. The study concludes that "Strengthening community awareness and promoting safe e-waste handling and disposal practices are essential to protect public health and the environment, " according to Ebrahim et al., Scientific Reports (2026).

• 423 households were surveyed in Kombolcha City in 2025, according to Ebrahim et al., Scientific Reports (2026).
• In July 2026 the authors reported 60% poor knowledge, 58.2% unfavorable attitudes, and 57.9% poor e-waste practices among respondents.
• Higher education increased the odds of good practice (AOR = 8.23, 95% CI: 1.45–46.79) and favorable attitudes increased the odds (AOR = 5.71, 95% CI: 2.96–11.05), according to the study.
• The authors recommend community awareness campaigns and stronger policy implementation to reduce public health risks, as stated in Scientific Reports (2026).

The study measured household e-waste knowledge, attitudes, and practices with a 2025 community-based cross-sectional survey and qualitative interviews, according to Ebrahim et al., Scientific Reports (published 26 July 2026).

According to the Scientific Reports article, the quantitative component used a standardized questionnaire on 423 households, analyzed with SPSS version 26, while the qualitative component used six focus group discussions and 30 key informant interviews analyzed thematically.

According to the authors, variables with p < 0.25 in bivariable analysis were entered into multivariable logistic regression and statistical significance was declared at p < 0.05, which produced adjusted odds ratios for education and attitude factors.

Researchers should use mixed-methods to capture both prevalence and reasoning, because Ebrahim et al., Scientific Reports (2026) combined quantitative KAP measures with six FGDs and 30 KIIs to explain why 57.9% of households had poor practices.

Program designers should prioritize education and attitude change, because the Kombolcha study found education (AOR = 8.23) and favorable attitudes (AOR = 5.71) were the strongest predictors of safer e-waste practices in 2025.

Policy teams should treat household behavior as a modifiable factor, because the authors conclude that "Strengthening community awareness and promoting safe e-waste handling and disposal practices are essential to protect public health and the environment" (Ebrahim et al., Scientific Reports, 2026).

For context on global risk and methods, see the UNEP Global E-waste Monitor for standardized metrics used in environmental health research.

The Kombolcha mixed-methods study (published 26 July 2026 in Scientific Reports) shows major gaps in household e-waste knowledge and practice that are driven by education and attitudes.

AI-enabled qualitative analysis shortens the path from FGDs and KIIs to actionable recommendations by automating transcription, extracting themes, and linking themes to survey segments, which helps programs target the 60% knowledge gap reported in the study.

If you run mixed-methods field studies or need faster synthesis for policy, Try Evidano for free to accelerate transcription, thematic coding, and evidence visualization.