Determinants of Citizens’ Intention to Participate in Self-Led Contact Tracing: Cross-Sectional Online Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: Contact tracing (CT) is a key intervention to contain outbreaks of communicable diseases. During large-scale outbreaks, public health services may lack the resources required to perform CT effectively. One way of mitigating this issue is to shift some of the tasks in CT normally performed by public health services to cases and their contacts, supported by digital tools. We refer to this as "self-led CT." However, while the effectiveness of the self-led CT inherently depends on the willingness and skills of citizens to participate, the determinants of citizens' intention to participate in self-led CT are not yet fully understood. OBJECTIVE: We aimed to identify determinants of Dutch citizens' intention to participate in self-led CT and assess their potential for behavioral change, so as to identify "behavior change targets," which may be used in the development and implementation of self-led CT to increase citizens' intention to participate. METHODS: In March 2022, we performed an online cross-sectional questionnaire study. The questionnaire was developed based on findings from a previous exploratory semistructured interview study and distributed among a Dutch consumer panel. Using all questionnaire items as potential predictors, we performed a random forest analysis to identify determinants of citizens' intention to participate in self-led CT. We then performed an Agglomerative Hierarchical Cluster Analysis to identify groups of related determinants that may be considered overarching behavior change targets. Finally, we used Confidence Interval-Based Estimation of Relevance and calculated the Potential for Change Indices to compare the potential for behavioral change of the selected individual determinants and determinant clusters. RESULTS: The questionnaire was completed by 3019 respondents. Our sample is representative of the Dutch population in terms of age, gender, educational level, and area of residence. Out of 3019 respondents, 2295 (76%) had a positive intention to participate in self-led CT. We identified 20 determinants of citizens' intention that we grouped into 9 clusters. In general, increasing citizens' trust in the digital tools developed for self-led CT has the highest potential to increase citizens' intention, followed by increasing the belief that using digital tools makes participating in self-led CT easier, reducing privacy-related concerns, and increasing citizens' willingness-and sense of responsibility-to cooperate in CT in general. CONCLUSIONS: Overall, Dutch citizens are positive toward participating in self-led CT. Our results provide directions for the development and implementation of self-led CT, which may be particularly useful in preparing for future, large-scale outbreaks.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".