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  • Francisca Fariña 1,
  • Juan Romero 1 and
  • Ramón Arce 2,*
  • et al.

Reviewer 1: Alemayehu Gebissa Reviewer 2: Yongsheng Qian Reviewer 3: Yueru Xu

Round 1

Reviewer 1 Report

- In Abstract: Please delete Objective,  Method,  Results and  Discussion. The sentences are fine and can stay as it is.

- Some abbreviations eg. (Ms, t, ns) under methods which are defined later under results, should have to be once defined under methods to improve the understandability

 

Author Response

Dear reviewer:

Thanks for you time and suggestions. We pass to answer your concerns.

- In Abstract: Please delete Objective,  Method,  Results and  Discussion. The sentences are fine and can stay as it is.

DONE

- Some abbreviations eg. (Ms, t, ns) under methods which are defined later under results, should have to be once defined under methods to improve the understandability

DONE in data analysis subsection and in the table notes.

 

The authors

 

Author Response File: Author Response.docx

Reviewer 2 Report

In this paper, the authors a field study to determine if traffic offenders were characterized by substance-related and addictive disorders, and behavioural addictions, and their prevalence in this population. The results revealed a more than problematic effect in drug addiction, alcohol consumption, and compulsive purchasing in the population of traffic offenders. The manuscript is well organized, and the results enrich the existing theoretical research models and have certain theoretical value. Therefore, I think the paper can be accepted. In addition, the following are the few comments, which may be included while revision.

 

1. The abstract should summarize the main methods, conclusions, and innovative of the paper as a whole. It is not recommended for the author to list and explain Objective, Method, Results, and Discussion separately.

2. The data sample size studied in the article is relatively small.

3. The author used statistical methods to analyze behavioral addition in relation to the effects for drivers included for traffic effects. However, why did these effects occur? What are the reasons for these effects?

4. “the probability of a false rejection of the null hypothesis was .349 i.e., the 34.9% in both”. What is the basis of this conclusion?

5. The results should be also compared with other existing work from the literature.

6. Based on the conclusions obtained, what are the author's suggestions for real-life traffic? Or does the result have any specific guiding significance for the actual traffic operation? This should be supplemented.

7. The list of references should be extended to include some recent papers as follow.

1) A multivalue cellular automata model for multilane traffic flow under lagrange coordinate.Computational and Mathematical Organization Theory.

2) The Journey from Traffic Offender to Severe Road Trauma Victim: Destiny or Preventive Opportunity?. PLOS ONE, 2015, 10.

3) State alcohol ignition interlock laws and fatal crashes. Traffic Injury Prevention, 2021, 22(8):589-592.

4) Persistence of addictive disorders in a first-offender driving while impaired population:context. JAMA Psychiatry, 2011, 68(11):1151-1157.

 

Author Response

Dear Reviewer:

Thanks for your time and suggestions that surely improve the paper. We pass to answer to your concerns (in the attached file the additions to text were highlisted in green).

  1. The abstract should summarize the main methods, conclusions, and innovative of the paper as a whole. It is not recommended for the author to list and explain Objective, Method, Results, and Discussion separately.

Deleted the Abstract subsections.

  1. The data sample size studied in the article is relatively small.

It is not exact. It depends of the statistical tool employed and the expected effect size. So, as for a medium effect size and the performed data analysis the sensitivity of the design for a total sample of 236 ranges from .80 to 1. In any case, the effect sizes would correct the effects of a small sample size.

  1. The author used statistical methods to analyze behavioral addition in relation to the effects for drivers included for traffic effects. However, why did these effects occur? What are the reasons for these effects?

The effect from the mean comparisons are for the population factor (offender vs. non-offender drivers [controls]) as it is displayed in the results section. And the effect for clinical disorders (significantly higher than expected in traffic offenders) is the prevalence of caseness. It is exhibited in results section.

  1. “the probability of a false rejection of the null hypothesis was .349 i.e., the 34.9% in both”. What is the basis of this conclusion?

The power of the results (1- β) was analyzed in the Table 3 and for internet and eating addiction was small (< .80). Then, the countermeasure, probability of a false rejection of the null hypothesis, is β. Added in text.

  1. The results should be also compared with other existing work from the literature.

DONE.

  1. Based on the conclusions obtained, what are the author's suggestions for real-life traffic? Or does the result have any specific guiding significance for the actual traffic operation? This should be supplemented.

We highlight the relevance of the intervention on the clinical assessment and intervention with offenders with a problematic addiction or a disorder. We also empathize the implications of the results for prevention and intervention programs.

  1. The list of references should be extended to include some recent papers as follow.

The literature was updated.

 

The authors

Author Response File: Author Response.docx

Reviewer 3 Report

In this paper, the authors works on the connection between the addiction (drug, internet, etc.) and accidents. This work enhance the understanding about the relevant fields and professional practice. This work is suitable to be published in sustainability. And some questions should be further explained before published.

1. Introduction, page1

In consequence, men were 4.04 (OR = 4.04[3.98, 4.10]) times more likely to sustain a fatal traffic accident; the elderly were 1.46 (OR = 1.46[1.44, 1.48]) more likely to suffer a fatal age-related accident; and young adults were 1.81 (OR = 1.81[1.79, 1.83] more likely to suffer a fatal age-related accident.

Some interval estimations are cited here, please point out the degree of confidence (95%?)

2. Measurement instruments, page 3

I suggest the author explaining the meaning of α here.

3.Method

In contrast, a significantly higher level of internet addiction was observed in unconvicted drivers with a magnitude of the effect above 63.68%, and with a probability of misclassification of the model (PSES) of 31.2%. Here, how do you calculate thr PSES? Give more details.

4.Limitations and future research

The samples of traffic offenders and the general population were matched in sex, age and time elapsed since the driving test, but other uncontrolled variables (strange variables) may have mediated the results.

I suggest that the author can test this issue through some small-scale samples if possible.

The English writing is fine

Author Response

Dear Reviewer:

Thanks for your time and suggestions that surely improve the paper. We pass to answer to your concerns (in the attached file the additions to text were highlighted in green).

  1. Introduction, page1

In consequence, men were 4.04 (OR = 4.04[3.98, 4.10]) times more likely to sustain a fatal traffic accident; the elderly were 1.46 (OR = 1.46[1.44, 1.48]) more likely to suffer a fatal age-related accident; and young adults were 1.81 (OR = 1.81[1.79, 1.83] more likely to suffer a fatal age-related accident.

Some interval estimations are cited here, please point out the degree of confidence (95%?)

DONE (Indeed your appreciation is correct and very pertinent. Thanks).

 

  1. Measurement instruments, page 3

I suggest the author explaining the meaning of α here.

Extended to “internal consistency: Cronbach’s”.

3.Method

In contrast, a significantly higher level of internet addiction was observed in unconvicted drivers with a magnitude of the effect above 63.68%, and with a probability of misclassification of the model (PSES) of 31.2%. Here, how do you calculate the PSES? Give more details.

Detailed in data analysis subsection.

4.Limitations and future research

The samples of traffic offenders and the general population were matched in sex, age and time elapsed since the driving test, but other uncontrolled variables (strange variables) may have mediated the results.

I suggest that the author can test this issue through some small-scale samples if possible.

We understand this concern that could be coped via cross-validation or with covariates. Nevertheless, it was not be submitted to proof as other potential mediators, as they are unknown, of the results were not measured, and the expected power is variable by variable insignificant. In other words, the core of the variance of the effect was measured by the study variables, but not all the variance is due to the measured variables. In any case, the power of these potential variables is small and rest on a large number of variables. So, the effect for each individual variable is insignificant.

 

The authors

Author Response File: Author Response.docx