The Factors Affecting the Decision to Participate in Voluntary Social Insurance of Vietnamese Employees: The Case of Tra Vinh Province
Bibliographic record
Abstract
The purpose of this study is to find out the factors affecting the decision on participation in voluntary social insurance of non-state sector employees. To propose such recommendations to the Vietnam Government and Social Insurance of Vietnam, to develop a voluntary insurance policy, improve social security for non-state workers in Vietnam. The analysis of factors affecting the decision to participate in voluntary social insurance of Vietnamese employees in the case of Tra Vinh province by using the method of primary data collection of 300 employees in Tra Vinh province; using multivariate regression methods. The study has found 9 factors such as: social security awareness, the attitude of the employees, knowledge of voluntary social insurance of the employees, the social influence of voluntary social insurance, income of employees, social media, voluntary social insurance policy, Adult’s health awareness in old age and moral responsibility affecting the decision to participate in voluntary social insurance of the employees in Tra Vinh province. Then, the authors have proposed implicational policies such as enhancing communication work for employees, building flexible social insurance policies, diversifying types of payments, Focusing on awareness education about Vietnam social security,…contributing to ensuring social security for Vietnamese employees when they reach the retirement age.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".