인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
The increased subscription and ownership of mobile phones have created opportunities to improve health, education, or economic outcomes, including mobile phone surveys (MPS) to collect health data. Most MPS used cross-sectional survey designs. We explored the potential of MPS to collect panel data using anonymous surveys with agreement in age and gender, and participants' retention across survey waves in three low- and middle-income countries (LMICs): Bangladesh, Colombia, and Uganda. Using random digit dialing, participants were recruited from 6 age-gender strata (i.e., 18-29-, 30-44-, and 45+-year-old males and females). Three interactive voice response survey waves were sent at two-week intervals. In Wave 1, the number of complete interviews in Bangladesh, Colombia, and Uganda was 2693, 5912, and 4813, respectively. In all waves, the proportion of 18-29-year-olds responding to the surveys was higher than that of 30-44- or 50+-year-olds. Bangladesh (83.7% in Wave 1) and Uganda (70.1% in Wave 1) had a higher proportion of males than females, while it was different in Colombia (45.6% in Wave 1). Regarding the reporting of age and gender in survey waves, we observed a high agreement in all three countries; the Kappa statistic was 0.89 (agreement: 93.7%) from Wave 1 to Wave 2 and 0.90 (agreement: 94.5%) from Wave 1 to Wave 3. In Wave 1, the response and refusal rates were, respectively, 0.26% and 0.19% in Bangladesh; 0.65% and 0.89% in Colombia; and 2.63% and 0.71% in Uganda. From Wave 1 to Wave 2, the attrition rate was 37.2% in Bangladesh, 43.7% in Colombia, and 39.2% in Uganda. From Wave 1 to Wave 3, the attrition rate was 64.2%, 62.8%, and 58.4% in Bangladesh, Colombia, and Uganda, respectively. Despite high attrition across survey waves, the agreement about responses was substantial in all countries and MPS has the potential to be implemented in LMICs. More research is required to improve the retention and increase enrollment in some sociodemographic groups (e.g., older people or women). Future studies could also be benefitted from adding validation questions to ensure the participation by the same respondent.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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