Measuring the importance of influencing factor for COVID-19 vaccination intention in China
Bibliographic record
Abstract
Background Vaccination is considered an effective approach to deter the spread of coronavirus disease (COVID-19). However, vaccine hesitancy is a common issue that makes immunization programs more challenging. To promote vaccination in a targeted and efficient way, this study aims to develop and validate a measurement tool for evaluating the importance of influencing factors related to COVID-19 vaccination intention in China, and to examine the demographic differences. Methods In study 1, we developed a Factor Importance Evaluation Questionnaire (FIEQ) based on semi-structured interview results and used exploratory factor analysis (EFA) to explore its factor structure. In study 2, we verified the four-factor structure of FIEQ by confirmatory factor analysis (CFA). We then administered FIEQ to Chinese participants and conducted a student t-test and analysis of variance to examine the differences in the importance evaluation of factors based on gender and educational level. Results In study 1, we developed a four-factor construct and retained 20 items after EFA (N = 577), with acceptable reliability (alpha = 0.87) and validity. In study 2, we found that the model fit was good (χ2 = 748.03 (162), p < 0.001, GFI = 0.949, RMSEA = 0.049, SRMR = 0.048, AGFI = 0.934), and reliability was acceptable (alpha = 0.730) (N = 1,496). No gender difference was found in factor importance. However, individuals with different educational levels reported significantly different importance evaluations of three factors, including perceived benefits and social norms (F = 3.786, p = 0.005), perceived influences from reference groups (F = 17.449, p < 0.001), and perceived risks (F = 2.508, p = 0.04). Conclusion This study developed and validated FIEQ for measuring the importance of influencing factors related to the COVID-19 vaccination intention in Chinese participants. Moreover, our findings suggest that the educational level may play a role in how individuals evaluate the importance of factors. This study provides insights into the concerns that individuals have regarding vaccination and offers potentially effective and targeted strategies for promoting COVID-19 vaccination.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".