Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Dear you

The paper is well written and is of great interest for the readers. 

Specific comments are in the attachment.

Comments for author File: Comments.pdf

Author Response

Reviewer 1

Review reports Manuscript ID: sustainability-2822835

A brief summary

The paper reviews organizations. Interesting examples and considerations are presented.

 

General concept comments

The paper addresses a relevant topic and summarizes data from a wide range of sources. The references are appropriate. Moreover, the manuscript is clear and presented in a well-structured manner. It covers a topic that needs to be explored. Relevant references are added and tables are easy to understand.

 

Ratings

 

Novelty: The question is original and well-defined.

• The results provide an advancement of the current

Scope: The work fits the journal scope*.

Significance: The results were interpreted appropriately.

Quality: The article is written appropriately.

• The data and analyses are presented
• The highest standards for presentation of the results

Interest to the Readers: The conclusions are interesting for the journal's readership.

• The paper attracts a wide

Overall Merit: There is an overall benefit to publishing this work.

• The work advances the current
• The authors address an important long-standing question with
• Do the authors present a negative result of a valid scientific hypothesis? Not

English Level: The English language is appropriate and understandable.

WE APPRECIATE YOUR  SUPPORT. WE ADDED SOME TEXT, BUT NOT A GREAT DEAL TO CLARIFY SOME ISSUES.

Reviewer 2 Report

Comments and Suggestions for Authors

Very interesting, informative, and accessible.

Section 2:

- You discuss the importance of communication early and often between experts and the public, but it is unclear to me how this would have averted an issue such as the example given about incinerating weapons. This example concludes with discussing the Army moving toward non-incineration methods, which makes it seem like the public was correct in this case and the communication issue is more related to shared decision-making.

- The example of Fukushima should include more details about what happened for those who may be unfamiliar, as well as the fact that it was precipitated by a natural disaster (as opposed to human error).

- Could you provide more detail about mistrust related to the COVID-19 pandemic? This is a very relevant example and may appeal to more readers than the personal example of the weapon incineration or more distant example of Fukushima.

Section 3:

- Is there no newer data about healthcare expenditure:GDP? The example provided is over 20 years old.

- Can you provide more specific information about healthcare outcomes in the US? e.g mortality, life expectancy, etc.

- The paragraph on the association of trust with digital services is not clearly linked to healthcare and seems out of place in a section about issues with the US healthcare system in its current form. Consider discussing the health insurance marketplace in the US or another example to bridge the gap.

Section 4:

- The example of deer overpopulation may benefit from additional analysis more clearly illustrating the link between the local/personalized approach and building trust. The way it is currently written, it seems like the community became more tolerant/apathetic over time rather than built trust.

- Can you provide an example of nonprofit organizations being "vulnerable to the challenge of dishonesty"?

- The discussion of reference 41 is confusing as it states that "half of those surveyed thought citizens were aware of local government issues" followed by "less than one in five believed citizens were aware of local issues".

- The last paragraph describes the wealth of studies on trust in the healthcare system but does not comment on overall findings or trends, instead stating that more studies are needed. It would be helpful to have an overarching statement about the themes emerging from these studies or shared results.

Section 5:

- Is there any data on the relationship of trust and the efficacy of HMPs? This would make the link between the two concepts more clear.

Section 6:

- What is "black and gray" literature? This reader was unfamiliar.

- It appears that the development of table 1 (community application of AI) was generate using AI. Please detail how the review process of the information from Quora was completed. Were there criteria for inclusion? Does this method itself raise any issues of trust in the results given the subsequent discussion?

- Narcan does not have to be in all capital letters as this makes it seem like an acronym rather than a branded drug name.

- This section focuses on AI but the examples given are about confirmation bias, community goals, and joint policy development. "Databases" are mentioned but it is not clear how AI plays a role and what lessons learned in the examples can be applied to building trust in AI. Please strengthen connection between these concept, especially as it is one of the main goals of this review ("using AI to supplement the gap between community needs and limited local resources").

Author Response

Reviewer 2

Very interesting, informative, and accessible.

THANKS FOR YOUR DETAILED REVIEW. WE ADDRESSED EVERY POINT, TRYING NOT TO ADD TOO MUCH TEXT. IN SEVERAL PLACES WE CUT TEXT.

Section 2:

- You discuss the importance of communication early and often between experts and the public, but it is unclear to me how this would have averted an issue such as the example given about incinerating weapons. This example concludes by discussing the Army moving toward non-incineration methods, which makes it seem like the public was correct in this case and the communication issue is more related to shared decision-making.

GOOD POINT THAT NEEDS CLARIFICATION. THE ISSUE IS THAT THE PARTIES STUBBORNLY REFUSED TO LISTEN TO EACH OTHER. TEXT WAS ADDED, LINES 107-115. 

- The example of Fukushima should include more details about what happened for those who may be unfamiliar, as well as the fact that it was precipitated by a natural disaster (as opposed to human error).

AGREED. THE BETTER DECISION WAS TO DROP THIS EXAMPLE AND REPLACE IT WITH A SIMILAR-SIZED SECTION ABOUT COVID-19, P.3, LINES 120-134. 

- Could you provide more detail about mistrust related to the COVID-19 pandemic? This is a very relevant example and may appeal to more readers than the personal example of the weapon incineration or more distant example of Fukushima.

YES, WE DROPPED THE FUKUSHIMA EXAMPLE AND ADDED ONE ABOUT COVID-19.

Section 3:

- Is there no newer data about healthcare expenditure: GDP? The example provided is over 20 years old.

OOPS!. THIS IS THE 2022 OECD INFORMATION. THE 2002 ONLINE 144 WAS A TYPO.  

- Can you provide more specific information about healthcare outcomes in the US? e.g. mortality, life expectancy, etc.

YES, THE SAME OECD REPORT COMPARES THE U.S. TO 37 OTHER NATIONS. U.S. OUTCOMES ARE NOT GOOD, ESPECIALLY GIVEN ALL THE MONEY SPENT. SEE LINES 148-151. 

- The paragraph on the association of trust with digital services is not clearly linked to healthcare and seems out of place in a section about issues with the US healthcare system in its current form. Consider discussing the health insurance marketplace in the US or another example to bridge the gap.

WE ADDED THE BUTCHER AND HUSSAIN PAPER WHICH ADDRESSES THIS ISSUE. WE COULD ADD MORE BUT WERE RELUCTANT TO KEEP ENLARGING THE SIZE OF THE PAPER. SEE LINES 232-233.

Section 4:

- The example of deer overpopulation may benefit from additional analysis more clearly illustrating the link between the local/personalized approach and building trust. The way it is currently written, it seems like the community became more tolerant/apathetic over time rather than built trust.

THIS IS PRINCETON (NJ), THE HOME OF SOME OF THE SMARTEST AND INVOLVED U.S. RESIDENTS. THEY CONSOLIDATED THE BORO AND THE SURROUNDING TOWNSHIP AFTER STUDYING IT FOR YEARS. IN OTHER WORDS, YOUR IDEA COULD BE CORRECT, BUT UNLIKELY IN THIS MUNICIPALITY. SEE LINES 274-279.

- Can you provide an example of nonprofit organizations being "vulnerable to the challenge of dishonesty"?

YES, BUT FRANKLY WE ARE RELUCTANT TO USE SPECIFIC NAMES BECAUSE SOME OF THESE HAVE REINVENTED THEMSELVES. EISENSTEIN’S PAPER GOES OVER SOME SPECIFIC CASES. WE ADDED HIS REFERENCE. SEE 295-296.  

- The discussion of reference 41 is confusing as it states that "half of those surveyed thought citizens were aware of local government issues" followed by "less than one in five believed citizens were aware of local issues".

YES, IT IS CONFUSING. WE REWROTE IT ON LINES 303-306. 

- The last paragraph describes the wealth of studies on trust in the healthcare system but does not comment on overall findings or trends, instead stating that more studies are needed. It would be helpful to have an overarching statement about the themes emerging from these studies or shared results.

WE SEE THE PROBLEM. WE DROPPED THE REFERENCE BECAUSE IT SIGNALS MORE WORK BUT DOESN’T ADD MUCH SUBSTANCE. EVENTUALLY, WE SHOULD GET SOME SUBSTANTIVE RESULTS. THE FACT IS THAT THE U.S. GOVERNMENT AND A FEW FOUNDATIONS ARE NOT USEFUL.  

Section 5:

- Is there any data on the relationship of trust and the efficacy of HMPs? This would make the link between the two concepts more clear.

NOT MUCH. FEINBERG’S STUDY, WHICH WE ADDED, IS A GOOD EXAMPLE OF WHAT IS POSSIBLE. LINES 367-375.

Section 6:

- What is "black and gray" literature? This reader was unfamiliar.

WE ADDED SOME EXAMPLES OF GRAY LITERATURE IN THE DISCUSSION OF THE QUORA DATA. LINES 427-434.  

- It appears that the development of Table 1 (community application of AI) was generated using AI. Please detail how the review process of the information from Quora was completed. Were there criteria for inclusion? Does this method itself raise any issues of trust in the results given the subsequent discussion?

YES, THE QUORA DATA ARE SEARCHED FOR TERMS, AND THE MOST IMPORTANT POINT IS THAT THEY DO NOT CONSTITUTE A RANDOM SAMPLE AS NOTED IN THE TEXT LINES 427-434.

- Narcan does not have to be in all capital letters as this makes it seem like an acronym rather than a branded drug name.

OKAY, CHANGED IT

- This section focuses on AI but the examples given are about confirmation bias, community goals, and joint policy development. "Databases" are mentioned but it is not clear how AI plays a role and what lessons learned in the examples can be applied to building trust in AI. Please strengthen the connection between these concepts, especially as it is one of the main goals of this review ("using AI to supplement the gap between community needs and limited local resources").

WE SEE THE POINT. WE ADDED SOME TEXT TO TRY TO MAKE THE CASE CLEARER. THREE MAJOR CHALLENGES OF WHICH SUSTAINING A PARTNERSHIP WE THINK IS THE MOST DIFFICULT, ESPECIALLY WHEN AI IS PART OF THE OBJECTIVE. THESE WON’T BE EASY. WE HOPE THAT COMES ACROSS ON LINES 498-527. WE’D LIKE TO SEE A STUDY OF HOW MANY COOPERATIVE AGREEMENTS STARTED OUT BEAUTIFULLY AND LASTED FOR AT LEAST A DECADE.

Reviewer 3 Report

Comments and Suggestions for Authors

The authors introduced a review of the United States healthcare system. The review article is written very well, but there still are some suggestions for the authors:

Although the authors provided much information on this issue, no review methods were applied or told the reader how the author surveyed the info. For example, what surveys are included in this research?

I suggest the author change the word "percent" to "%" so it is easier to read.

 

Author Response

Reviewer 3

The authors introduced a review of the United States healthcare system. The review article is written very well, but there still are some suggestions for the authors:

Although the authors provided much information on this issue, no review methods were applied or told the reader how the author surveyed the info. For example, what surveys are included in this research?

YES, AN IMPORTANT POINT. WE ADDED TEXT ON LINES 163-168. ONLY A FEW ORGANIZATIONS HAVE COLLECTED THE KIND OF HISTORICAL DATA WE NEED.  

I suggest the author change the word "percent" to "%" so it is easier to read.

YES, WE MADE THIS CHANGE WHEN THE NUMBERS ARE ENCLOSED (62%). HOWEVER, OUR UNDERSTANDING IS THAT PERCENT SHOULD BE WRITTEN OUT WHEN IT IS PART OF TEXT. WE ARE HAPPY TO DEFER TO THE EDITOR.

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

Thank you for your revisions. I have no further recommendations.