Regression results for various groups based on lab size.

Regression results for various groups based on lab size.</p

S3 File -

A. Probability calculation of a scenario–An example. B. Probability calculation for all scenarios. (ZIP)</p

Regression results for a conditional logit model.

ObjectivesTo explore the key components when designing best practice inspection interventions, so as to induce high compliance with safety guidelines for laboratory workers.MethodsFive key components of an inspection intervention, identified from a focus group discussion, were used as the attributes of a discrete choice experiment (DCE). In the DCE, participants were presented with two hypothetical scenarios and asked to choose the scenario in which they were more willing to comply with the laboratory safety guidelines. Data were collected from 35 clinical laboratories in seven healthcare institutes located in Chengdu, China. In total, 188 laboratory workers completed the DCE. The collected data were analyzed using conditional logit regression and latent class analysis.ResultsFive key attributes were identified as the most important ones to best ensure laboratory safety: the inspector, the inspection frequency, the inspection timing, the communication of the inspection outcome, and a follow-up with either a reward or a punishment. By investigating the laboratory workers’ responses to the attributes, properly implementing the five attributes could improve the workers’ compliance from 25.86% (at the baseline case) to 74.54%. Compliance could be further improved with the consideration of the laboratory workers’ heterogeneous reactions. In this study, two classes of workers, A and B, were identified. Compliance percentages for Classes A and B would be improved to 85.48% and 81.84%, respectively, when the key attributes were properly implemented for each class. The employment type and the size of the laboratory could be used to predict class membership.ConclusionThe findings indicate the importance of an employee-centered approach in encouraging a worker’s compliance. This approach also supports the design of tailored interventions by considering the laboratory workers’ heterogeneous responses to the interventions.</div

Attributes and levels used in the discrete choice experiment.

Attributes and levels used in the discrete choice experiment.</p

Regression results for various groups based on employment type.

Regression results for various groups based on employment type.</p

Regression results for various groups based on age.

Regression results for various groups based on age.</p

S1 Questionnaire -

ObjectivesTo explore the key components when designing best practice inspection interventions, so as to induce high compliance with safety guidelines for laboratory workers.MethodsFive key components of an inspection intervention, identified from a focus group discussion, were used as the attributes of a discrete choice experiment (DCE). In the DCE, participants were presented with two hypothetical scenarios and asked to choose the scenario in which they were more willing to comply with the laboratory safety guidelines. Data were collected from 35 clinical laboratories in seven healthcare institutes located in Chengdu, China. In total, 188 laboratory workers completed the DCE. The collected data were analyzed using conditional logit regression and latent class analysis.ResultsFive key attributes were identified as the most important ones to best ensure laboratory safety: the inspector, the inspection frequency, the inspection timing, the communication of the inspection outcome, and a follow-up with either a reward or a punishment. By investigating the laboratory workers’ responses to the attributes, properly implementing the five attributes could improve the workers’ compliance from 25.86% (at the baseline case) to 74.54%. Compliance could be further improved with the consideration of the laboratory workers’ heterogeneous reactions. In this study, two classes of workers, A and B, were identified. Compliance percentages for Classes A and B would be improved to 85.48% and 81.84%, respectively, when the key attributes were properly implemented for each class. The employment type and the size of the laboratory could be used to predict class membership.ConclusionThe findings indicate the importance of an employee-centered approach in encouraging a worker’s compliance. This approach also supports the design of tailored interventions by considering the laboratory workers’ heterogeneous responses to the interventions.</div

Regression results for various groups based on years of work experience.

Regression results for various groups based on years of work experience.</p

Two-class membership: Regression results of Class A (31.91%) against the demographic characteristics, with Class B (62.09%) as the reference level.

Two-class membership: Regression results of Class A (31.91%) against the demographic characteristics, with Class B (62.09%) as the reference level.</p

Regression results for various groups based on gender.

Regression results for various groups based on gender.</p