From: Harry Elston <harry**At_Symbol_Here**MIDWESTCHEMSAFETY.COM>
Subject: Re: [DCHAS-L] "Good science" in publications
Date: Mon, 14 Oct 2019 09:10:21 -0500
Reply-To: harry**At_Symbol_Here**midwestchemsafety.com
Message-ID: 002901d58299$244d4ab0$6ce7e010$**At_Symbol_Here**midwestchemsafety.com
In-Reply-To <899D264C-7DC3-46C3-94D6-B1CF01DEC09A**At_Symbol_Here**keene.edu>


I can tell you how I approach it:

When data, however acquired, is presented, it must be presented in such a
way that the reviewer can look at it and arrive at the same or similar
conclusion(s). The acquisition method must be sound, and for the safety
world that usually means that it was:
a) Acquired in accordance with a nationally recognized method (i.e. NIOSH
MAM, ANSI method, etc.) or;
b) A reasonable explanation is made why it wasn't acquired in accordance
with a nationally recognized method.

I always cringed at the "we threw the sample on our in-lab GC and got these
results" type papers, but sometimes they would get through.

Presentation should/must include some statement of uncertainty associated
with the data, being total uncertainty or analytical uncertainty. Graphical
representations must include uncertainty ("error bars.") The conclusions
drawn from that data must also account for uncertainty. The red ink of my
reviewer's pen freely flows when uncertainty is not mentioned. I prefer the
statement "indistinguishable from a known blank sample" in place of
"background", but I'm not a stickler on that for other people's writing; and
I had no small number of heated discussions with IH-types over what "zero"
means in a sample.

Regarding a "soft data" - data such as survey results and the like: I
always wanted to have the author include the actual survey questions so that
reviewers could look for inherent bias or leading in the questions. I
recognize that writing questions that do not have inherent bias or lead the
responder to a certain viewpoint is very difficult. Likewise, the data
from surveys can be very difficult to interpret.

H

-----Original Message-----
From: ACS Division of Chemical Health and Safety On
Behalf Of Stuart, Ralph
Sent: Monday, October 14, 2019 7:48 AM
To: DCHAS-L**At_Symbol_Here**PRINCETON.EDU
Subject: [DCHAS-L] "Good science" in publications

Last month, Mary Beth Mulcahy let the list know of the opportunity to be a
reviewer for the ACS Chemical Health & Safety journal and suggested people
who are interested in this chance for professional development take
advantage of the ACS Reviewer Lab (https://www.acsreviewerlab.org/). I
completed this on line course last week and have a philosophical question
for the DCHAS membership based on the guidelines suggested there.

The Reviewer Lab suggests that one of the reviewer's jobs is to assess
whether the article contains "good science". I wonder if CHAS members
approach this question in the same way as is outlined in the course, which
relies on the traditional description of the scientific method. The
Reviewers Lab suggests that the reader consider the author's Experimental
Approach, Design and Execution along with Data Analysis methods as the
primary criteria for assessing the value of an article about chemical health
and safety issues.

Since health and safety issues often involve unpredicted or unexplained
outcomes of chemical processes and/or the complex cultural system associated
with process safety decision-making, I often find myself interested in
questions that go beyond the criteria mentioned above. My usual interest in
reading a peer reviewed article is in understanding the nature of the work
that is being conducted and assessing how it might impact my strategies in
supporting safer lab practice in specific contexts. These questions often do
not align with the technically-oriented questions suggested above. I wonder
if other CHAS members take a similar approach in reviewing the health and
safety oriented scientific literature?

Thanks for any thoughts on this question.

- Ralph


Ralph Stuart, CIH, CCHO
Environmental Safety Manager
Keene State College
603 358-2859

ralph.stuart**At_Symbol_Here**keene.edu

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