Factors Affecting Nonresponse Among Female Participants in the Korea Nurses’ Health Study: Longitudinal Cohort Survey Study
Bibliographic record
Abstract
BACKGROUND: The major drawback of a cohort study design is the loss to follow-up, which increases selection bias and threatens external validity, particularly in online surveys. It is important to identify factors beyond population or demographics that influence nonresponse rates in cohort studies. OBJECTIVE: This study aimed to examine the nonresponse rate and associated factors over a 10-year follow-up period among female participants in the Korea Nurses' Health Study using data from the initial and subsequent surveys. METHODS: The Korea Nurses' Health Study recruited 20,613 female nurses in 2013 using simple random sampling. The participants were followed up 10 times through 2022. We identified the demographic, work-related, survey-related, and psychological characteristics of nonresponding nurses during the 10-year follow-up and compared them with those who continued to participate. Descriptive statistics, chi-square tests, and multivariate logistic regression models were used for the analysis. RESULTS: The nonresponse rate of the follow-up surveys from the 2nd to the 11th survey varied between 25.5% (5258/20,613; second survey) and 61.2% (12,620/20,613; sixth survey). The influence of age, education, and the usability of survey websites on nonresponse lasted up to the 11th survey. Nurses in their 20s were less likely to respond to the follow-up surveys than those in their 30s. Those who had an associate degree and neutral feelings about the usability of the survey websites were less likely to respond to the follow-up surveys than those who were satisfied with the initial survey. The influence of geographical region, hospital size, and psychological factors-including stress, fatigue, and sleep disturbance-was evident from the second to the sixth survey. CONCLUSIONS: When designing and recruiting female nurse participants for community-based cohort studies, researchers should consider the factors that influence nonresponse and adopt tailored strategies based on demographic characteristics. In addition, improving the usability of survey websites is recommended to reduce nonresponses at follow-up in cohort studies involving female participants. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-DOI: https://doi.org/10.4178/epih.e2024048.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.274 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".